data technical lead

Venatore

<div>About Us 
Venat&ocirc;re is a mission-driven government contractor based in Tampa, FL, supporting federal and civilian agencies with secure cutting-edge technology, cybersecurity, and mission support services. Our teams are trusted in demanding environments because we integrate with client missions, combining technical depth, operational precision, and innovative thinking to deliver meaningful results where they matter most.
About the Job 
The Data Scientist Lead is responsible for leading the organization's data science, data engineering, and analytics functions in support of mission-critical military operations. This senior technical leadership role oversees the design, development, and operationalization of advanced data analytics, artificial intelligence, and machine learning capabilities that enhance data-driven decision-making across the command. Working closely with senior government stakeholders, including the Chief Data Officer (CDO) and Executive Data Officer (XDO), the Data Scientist Lead translates operational requirements into scalable technical solutions while providing strategic direction, technical oversight, and mentorship to a multidisciplinary team. This position requires the ability to manage multiple priorities in a dynamic, mission-focused environment and may require occasional domestic and international travel.
Responsibilities
Leadership &amp; Strategic Oversight<ul><li>
Lead and provide technical oversight for data engineering, data science, analytics, and predictive analytics teams in support of operational mission objectives.</li><li>
Establish strategic priorities for data management, advanced analytics, AI/ML, and data engineering initiatives.</li><li>
Serve as the senior technical advisor on data science, machine learning, and analytics capabilities.</li><li>
Mentor and develop technical staff while promoting best practices, innovation, and continuous improvement.</li><li>
Manage multiple concurrent projects, priorities, and deliverables in a fast-paced operational environment.</li></ul>
Data Science &amp; Artificial Intelligence<ul><li>
Lead the development, validation, deployment, and operationalization of advanced statistical models and AI/ML solutions.</li><li>
Apply supervised, unsupervised, reinforcement learning, and generative AI techniques, including Large Language Models (LLMs) and multimodal AI, to solve complex mission challenges.</li><li>
Guide the full data science lifecycle, including data exploration, feature engineering, model development, validation, deployment, and performance monitoring.</li><li>
Evaluate emerging AI and machine learning technologies for mission applicability and operational effectiveness.</li></ul>
Data Engineering &amp; Architecture<ul><li>
Oversee the design, implementation, and maintenance of secure, scalable, and fault-tolerant data pipelines supporting structured and unstructured data.</li><li>
Direct ETL/ELT processes, data integration, data warehousing, and modern data lake/lakehouse architectures.</li><li>
Ensure data quality, governance, accessibility, and compliance throughout the data lifecycle.</li><li>
Support enterprise data management strategies and architecture modernization initiatives.</li></ul>
Stakeholder Engagement &amp; Mission Support<ul><li>
Collaborate with the USCENTCOM Chief Data Officer (CDO), Operations Directorate Executive Data Officer (XDO), and other government stakeholders to define operational data requirements.</li><li>
Participate in governance boards, technical working groups, and strategic planning forums.</li><li>
Translate complex technical findings into clear, actionable recommendations for senior military leadership and non-technical stakeholders.</li><li>
Ensure analytical capabilities directly support operational planning, mission execution, and strategic decision-making.</li></ul>
Analytics &amp; Technology<ul><li>
Guide the use of SQL, Python, R, and other analytical tools to develop data-driven solutions.</li><li>
Oversee implementation and utilization of visualization platforms such as Power BI, Tableau, Qlik, Palantir Foundry, and Maven Smart System (MSS).</li><li>
Support enterprise analytics platforms, distributed data processing technologies, and database environments including Apache Spark, Databricks, Hadoop, Oracle, PostgreSQL, MySQL, and Microsoft SQL Server.</li><li>
Promote adoption of modern analytics methodologies and industry best practices.</li></ul>
Required Qualifications<ul><li>
U.S. Citizenship is required.</li><li>
Active Top Secret security clearance with SCI eligibility.</li><li>
Bachelor's degree in Data Science, Analytics, Computer Science, Information Technology, or a related technical discipline.</li><li>
More than 10 years of progressively responsible experience in data science, analytics, or data engineering, including significant leadership experience.</li><li>
Demonstrated experience supporting Military, Federal Government, or Intelligence Community organizations.</li><li>
Proven experience gathering, implementing, and managing data science requirements for senior government stakeholders.</li><li>
Expertise developing and deploying advanced AI/ML solutions using frameworks such as TensorFlow, PyTorch, and Scikit-learn.</li><li>
Advanced proficiency in Python and/or R for data engineering, automation, analytics, and machine learning development.</li><li>
Experience designing and maintaining ETL/ELT pipelines, data warehouses, and data lake/lakehouse environments.</li><li>
Experience with enterprise data visualization and analytics platforms, including Power BI, Tableau, Qlik, Palantir Foundry, or Maven Smart System (MSS).</li><li>
Experience working with distributed data processing technologies such as Apache Spark, Databricks, and Hadoop.</li><li>
Experience administering or utilizing relational database platforms including SQL Server, Oracle, PostgreSQL, and MySQL.</li><li>
Strong written and verbal communication skills with demonstrated experience briefing senior military or government leadership.</li><li>
Ability to travel occasionally to domestic and international locations.</li></ul>
Preferred Qualifications<ul><li>
Master's degree in Data Science, Computer Science, Analytics, Information Technology, or another related technical discipline.</li><li>
Experience implementing enterprise data and analytics solutions within Azure, AWS, or Google Cloud environments.</li><li>
Strong knowledge of Department of Defense data strategies, governance frameworks, cybersecurity requirements, and information assurance standards.</li><li>
Experience with ESRI ArcGIS technologies and geospatial analytics.</li><li>
Demonstrated success leading cross-functional technical teams in fast-paced operational environments.</li><li>
Experience supporting AI/ML modernization initiatives within Department of Defense or Intelligence Community organizations.</li></ul>
Benefits 
Venat&ocirc;re offers a competitive benefits package designed to support the well-being of our employees, including:<ul><li>
Paid Time Off (PTO)</li><li>
10 Federal Holidays</li><li>
401(k) with company matching</li><li>
Medical, dental, and vision insurance</li><li>
Paid military leave</li></ul>
Venat&ocirc;re is a woman owned small business and an equal opportunity employer. We consider qualified applicants without regard to disability or protected veteran status.</div>

Data Scientist Lead

Group AMANA

<div>
 
 Amana is seeking an experienced Data Platform Lead with deep expertise in Databricks, the Microsoft Azure ecosystem, and modern lakehouse architectures. The ideal candidate will play a central role in Amana's ongoing platform refresh, moving workloads from Synapse Analytics to a Databricks lakehouse with Unity Catalog, and building the technical foundation that powers analytics, AI, and governance across the enterprise. This role sits within Amana's Data Science, Governance &amp; AI Centre of Excellence, working alongside architects, engineers, analysts, data scientists, and AI engineers, with future growth opportunities into senior platform leadership.
 
 
 
 
 
 
 Why This Role Matters
 
 
 
 Amana operates across capital-intensive sectors where data reliability, cost discipline, and speed of insight are business-critical. The enterprise data platform is the backbone of every downstream analytics, AI, and governance initiative &mdash; from ERP-integrated reporting and predictive models to Agentic AI and Copilot rollouts across the group.
 
 
 You will be at the technical centre of this transformation, designing, building, and hardening the lakehouse that the rest of the function depends on.
 
 
 
 
 
 
 Core Responsibilities
 
 
 
 
 Lakehouse Architecture &amp; Delivery
 
 
 <ul>
 <li>
 Design and deliver end-to-end Databricks lakehouse components &mdash; ingestion, transformation, semantic layer, and serving.
 </li>
 <li>
 Set and enforce Delta Lake and medallion architecture standards across bronze, silver, and gold layers.
 </li>
 <li>
 Lead the technical execution of the Synapse Analytics to Databricks migration, including parallel run, cutover, and decommissioning.
 </li>
 <li>
 Contribute to and sign off on reference designs, integration patterns, and architecture decision records.
 </li>
 </ul>
 
 
 
 
 
 Data Engineering &amp; Integration
 
 
 <ul>
 <li>
 Build and maintain robust ingestion and transformation pipelines from Amana's core enterprise systems: Oracle Fusion (Finance, HCM, P2P, CRM), Hyperion, CMiC, project and operations systems, SharePoint, and Power Platform.
 </li>
 <li>
 Apply strong CI/CD, DataOps, and infrastructure-as-code practices using Git, Azure DevOps, and Terraform or Bicep.
 </li>
 <li>
 Ensure platform reliability through monitoring, alerting, and structured incident response.
 </li>
 </ul>
 
 
 
 
 
 AI/ML &amp; Analytics Enablement
 
 
 <ul>
 <li>
 Build and operate feature store, MLOps, and LLMOps foundations on Databricks.
 </li>
 <li>
 Enable production-grade RAG, agent, and Copilot patterns in partnership with the Data Scientist and AI Engineer.
 </li>
 <li>
 Deliver Power BI semantic models, certified datasets, and self-service guardrails supporting the Amana Digital Academy and enterprise adoption.
 </li>
 </ul>
 
 
 
 
 
 Data Governance &amp; Trustworthy Data
 
 
 <ul>
 <li>
 Implement lineage, cataloguing, access control, data quality, and PDPL controls inside the platform using Unity Catalog and Microsoft Purview.
 </li>
 <li>
 Partner with the Data Governance Office to translate live policies into enforceable platform controls.
 </li>
 <li>
 Support regulatory, ethical, and internal compliance obligations across the data estate.
 </li>
 </ul>
 
 
 
 
 
 Cloud &amp; Cost Discipline
 
 
 <ul>
 <li>
 Own FinOps hygiene on the platform: cluster policies, workload tiering, rightsizing, and chargeback readiness.
 </li>
 <li>
 Translate cloud cost outcomes into commercial terms understood by finance and business stakeholders.
 </li>
 </ul>
 
 
 
 
 
 Collaboration &amp; Technical Leadership
 
 
 <ul>
 <li>
 Work closely with the Data Architect, Data Engineer, Data Analyst, Data Scientist, and AI Engineer to set and uphold technical standards.
 </li>
 <li>
 Provide hands-on technical guidance and code review across the team.
 </li>
 <li>
 Translate business requirements into technical designs with clear delivery outcomes.
 </li>
 </ul>
 
 
 
 
 
 Required Qualifications
 
 
 <ul>
 <li>
 Minimum of 7 years in data engineering or data platform work, with strong hands-on delivery experience.
 </li>
 <li>
 At least 3 years hands-on with Databricks, Apache Spark, and Delta Lake at enterprise scale. Snowflake or BigQuery experience is acceptable where the CV shows a real Databricks migration.
 </li>
 <li>
 Contributed to the build or rebuild of an enterprise data platform serving multiple business domains.
 </li>
 <li>
 Deep hands-on experience with the Azure data stack: Databricks, ADLS, Azure Data Factory, Synapse, Azure SQL, Fabric.
 </li>
 <li>
 Direct experience integrating heavy ERP sources: Oracle Fusion, Hyperion, CMiC, or equivalent estates.
 </li>
 <li>
 Governance shipped to production using Unity Catalog or Microsoft Purview, with PDPL or GDPR controls operational, not theoretical.
 </li>
 <li>
 Practical FinOps outcomes on cloud spend.
 </li>
 <li>
 Strong on CI/CD, Git, Azure DevOps, and infrastructure-as-code (Terraform or Bicep).
 </li>
 <li>
 Excellent written and verbal communication skills across technical and business audiences.
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications
 
 
 <ul>
 <li>
 Direct experience of a Synapse Analytics to Databricks migration.
 </li>
 <li>
 Exposure to MLOps and LLMOps stacks: MLflow, feature store, vector stores, RAG, agent frameworks.
 </li>
 <li>
 Familiarity with Power BI semantic models and enterprise BI governance.
 </li>
 <li>
 Streaming and event-driven experience: Kafka, Event Hubs, Structured Streaming.
 </li>
 <li>
 Multi-entity, multi-country group exposure with mixed data residency considerations.
 </li>
 <li>
 Industry experience in construction, engineering services, manufacturing, energy, or logistics.
 </li>
 </ul>
 
 
 
 
 
 Certifications
 
 
 
 
 
 Mandatory:
 
 
 
 <ul>
 <li>
 
 Databricks Certified Data Engineer Professional
 
 (Candidates holding the Associate level will be considered on the condition that Professional is achieved within 6 months of joining.)
 </li>
 </ul>
 
 
 Preferred (one or more):
 
 
 <ul>
 <li>
 
 Databricks Certified Machine Learning Professional
 
 </li>
 <li>
 
 Databricks Certified Generative AI Engineer Associate
 
 </li>
 <li>
 
 Microsoft Certified: Azure Data Engineer Associate
 
 (DP-203 / DP-700)
 </li>
 <li>
 
 Microsoft Certified: Fabric Analytics Engineer Associate
 
 (DP-600)
 </li>
 <li>
 
 Microsoft Certified: Azure Solutions Architect Expert
 
 </li>
 <li>
 
 Certified Data Management Professional (CDMP)
 
 &mdash; DAMA
 </li>
 </ul>
 
 
 Education
 
 
 
 M.Tech / B.E. / B.Tech (Computer Science, Information Systems, IT) / MCA / MCS.
 
</div>

Data Platform Lead

Everience Benelux

<div>Company Description 
Everience is an international consulting group delivering AI-augmented digital services and placing people at the heart of the AI revolution.
With a presence in Europe, Africa, Asia and America, Everience offers its 4,000-strong workforce the most demanding and stimulating environment in which to transform and develop their skills, learning about new AI-based roles and building their future employability.
Through its Symbiotic Academy the group offers a unique hub for training, practical application and exchange where everyone can experiment, learn, and progress in the fields of artificial intelligence and data.
In accordance with its core purpose of orchestrating the symbiotic relationship between humans and AI in the workplace, Everience is making the augmented employee the driving force of a &ldquo;symbiotic age&rdquo;, where AI enhances talents and opens up new career opportunities. 
Job Description 
Mandatory Skills &amp; Experience
We are looking for a MarTech Data Engineering Lead to drive the development and evolution of a modern Marketing Technology platform for a leading organisation within the banking sector. Data is at the core of enabling personalised customer experiences and marketing capabilities. As the Engineering Lead, you will define the data engineering and integration strategy, working closely with business stakeholders, data analysts, data scientists, and enterprise architects to translate business requirements into scalable technical solutions.
Customer Data Platforms (CDP)<ul><li>
5+ years of experience working with Customer Data Platforms (CDPs).</li><li>
Proven expertise in customer identity management, profile unification, and audience segmentation strategies.</li><li>
Strong understanding of customer data activation and omnichannel marketing ecosystems.</li><li>
Experience working with flexible or open-schema CDP solutions is highly desirable.</li></ul>
Data Architecture &amp; Engineering<ul><li>
Strong background in Data Engineering and data integration.</li><li>
Experience designing and implementing scalable data integration architectures.</li><li>
Solid understanding of:<ul><li>
Data modelling</li><li>
Customer identity resolution</li><li>
Data governance</li><li>
Data quality frameworks</li><li>
Event-driven architectures</li><li>
API-based integrations</li></ul></li><li>
Experience working with cloud-based data platforms, preferably Google Cloud Platform (GCP).</li></ul>
Technical Leadership &amp; Integration<ul><li>
Proven experience leading engineering teams or technical delivery teams.</li><li>
Ability to translate complex business requirements into technical solutions for engineering teams.</li><li>
Comfortable working with business stakeholders, architects, and cross-functional teams.</li><li>
Experience integrating Customer Data Platforms with:<ul><li>
CRM platforms</li><li>
Marketing Automation platforms</li><li>
Analytics solutions</li><li>
Consent Management platforms</li><li>
Customer Engagement platforms</li></ul></li><li>
Experience collaborating with third-party vendors, reviewing technical designs, and challenging solution proposals where appropriate.</li></ul> 
Qualifications 
Soft Skills<ul><li>
Excellent communication and stakeholder management skills.</li><li>
Ability to explain complex technical concepts to both technical and non-technical audiences.</li><li>
Strong analytical thinking and problem-solving abilities.</li><li>
Demonstrated ownership, accountability, and a proactive approach to delivery.</li><li>
Experience working within Agile delivery environments.</li><li>
Ability to thrive in complex, international, and multidisciplinary teams, ideally within the financial services or banking sector.</li></ul> 

Additional Information<div></div> 
All our positions are open to both women and men and are, of course, open to people with disabilities.</div>

MarTech Data Engineering Lead (m/w/d)

L'Oréal

Unleash Your Potential at L'Oréal's Beauty Tech!
 
Who Are We?
 
For 115 years, L’Oréal, the world’s leading beauty player, has devoted itself to one thing only: fulfilling the beauty aspirations of consumers around the world.
 
For more than a century, L’Oréal has devoted itself solely to one business: Beauty. Present in 150 countries across five continents and with €42 billion consolidated sales, L'Oréal is the global industry leader. With 37 global beauty brands across four divisions, L’Oréal offers beauty for each covering all beauty categories and catering to all beauty desires. With the acquisition of the Australian brand Aēsop in 2023, the Group continues to expand its portfolio through targeted acquisitions as part of its drive to create the future of beauty.
 
Today, L’Oréal includes more than 2,000 tech professionals and is constantly growing. Beauty Tech is changing the game and leading the shift towards new consumer realities and a digital disruption. Championing Beauty Tech, we invent the beauty of the future while becoming the company of the future.
 
Beauty Tech is how we know our consumers intimately, augmenting their beauty journeys with unparalleled diverse and sustainable experiences. Beauty Tech equips the Group with the key assets it needs to conquer this new world, where Tech has become strategic. With this ambition, L’Oréal continues to recruit diverse, innovative, skilled and passionate minds in different tech domains such as Data, Digital, Cloud, Cyber Security, IT Architecture, DevOps, Applications and Infrastructure.
 
A Day in the Life of Technical Lead - Python, SQL & AI-Augmented Engineering
 
As the Data Technical Lead, you are the hands-on master of the data factory. You will translate the Solution Architect’s high-level data strategy into robust, production-grade code. Your mission is to lead a squad of data engineers in building scalable ETL and Reverse ETL pipelines using Python, SQL, and Airflow, while mandating the use of AI coding assistants to automate pipeline generation and query optimization.
 
In this role, You will..
 
1. AI-Driven Data Engineering (Mandatory)
 <ul>
<li>AI Velocity: Standardize the use of GitHub Copilot, Cursor, or ChatGPT to generate complex SQL transformations, Python scripts, and PySpark logic.</li>
<li>Query Optimization: Use AI-assisted tools to refactor legacy SQL for performance and cost efficiency (e.g., reducing BigQuery/Snowflake compute costs).</li>
<li>Unit Testing for Data: Leverage AI to generate exhaustive test suites for data validation and schema drift detection.</li>
</ul>
2. ETL & Reverse ETL Mastery
 <ul>
<li>Orchestration: Design and maintain complex, idempotent Airflow DAGs. Ensure high availability and observability of schedules.</li>
<li>Streaming & Batch: Implement data processing using Google Cloud Dataflow (Apache Beam) or Spark for massive-scale transformations.</li>
<li>Reverse ETL: Bridge the gap between the Data Warehouse and Business Apps (Salesforce, Braze, Google Ads) to ensure "Data Activation" is seamless and reliable.</li>
</ul>
3. Python & SQL Excellence (The "How")
 <ul>
<li>Pythonic Patterns: Develop custom Python libraries and operators for the data team to ensure DRY (Don't Repeat Yourself) principles across all pipelines.</li>
<li>SQL Governance: Own the "Gold Standard" for SQL performance. Conduct rigorous code reviews focusing on window functions, partitioning, and indexing.</li>
</ul>
4. Partnership with Solution Architect
 <ul>
<li>From Blueprint to Build: Take the Architect's ERD (Entity Relationship Diagram) and turn it into optimized Physical Tables and views.</li>
<li>Operational Integrity: Implement the CI/CD strategy for data (Git, dbt, or similar) to move from development to production without downtime.</li>
</ul>
What are we looking for?
 
Technical Requirements
 <ul>
<li>AI Tooling: Expert proficiency in GitHub Copilot or Claude Code for accelerating Python and SQL development.</li>
<li>Data Stack: Expert knowledge of Python 3.x and advanced SQL (analytical functions, recursive CTEs).</li>
<li>Orchestration: 3+ years of experience with Apache Airflow.</li>
<li>Cloud Processing: Proven experience with Google Cloud Dataflow (Apache Beam) or similar managed services (Dataproc, AWS Glue).</li>
<li>Data Concepts: Expert in ELT/ETL patterns, Data Lakehouse architecture, and Reverse ETL strategies.</li>
</ul>
Soft Skills & Qualifications
 <ul>
<li>Experience: 7+ years in Data Engineering, with 2+ years leading a squad.</li>
<li>Pragmatism: Ability to balance "Perfect Code" with "Business Velocity" using AI.</li>
<li>Leadership: Experience mentoring junior data engineers in writing clean, scalable Python.</li>
<li>Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.</li>
</ul>
What’s In It for You?
 
<ul>
<li>Working with cutting edge Technology, empowering employees with new age learning, global exposure, and opportunities to build future-ready careers.</li>
<li>A flexible and modern workplace, enabling teams to perform at their best through a smart hybrid model that supports balance and autonomy. A 3 Day in Office, 2 Day Work from Home setup.</li>
<li>Employee support at every life stage, with inclusive and progressive parental policies that help individuals and families thrive.</li>
<li>Holistic wellbeing offerings - personalized health benefits and strong mental wellness support to ensure employees feel their best.</li>
<li>Reward and Recognition opportunities, long-term incentives, and opportunities to share in L’Oréal’s collective success.</li>
<li>L'Oreal is an Equal Opportunity Employer and takes pride in a diverse environment</li>
</ul>
We would love to find out more about you as a candidate and we do not discriminate in recruitment, hiring, training, promotion, or other employment practices. The beauty we find in our differences gives us the freedom to go beyond. That’s the beauty of L’Oréal.

Technical Lead – Python, SQL & AI-Augmented Engineering

ICAEW

<div>
 
 At The Institute of Chartered Accountants England &amp; Wales (ICAEW), you'll be part of an organisation that's shaping the future of business, finance and the accountancy profession on a global scale. Our bold
 
 Direction 2030
 
 strategy puts members, innovation, sustainability and trust at the heart of everything we do; creating an exciting, forward looking environment where your work has real impact. We invest in our people through our benefits package, continuous development and a supportive, inclusive culture that empowers you to grow and thrive. If you're looking for a role with purpose, influence and opportunity, ICAEW is a place where your future can truly take shape.
 
 
 
 
 We're looking for a Data Engineering Lead to join our team in Milton Keynes on a permanent basis. This role offers a salary of &pound;75,000 - &pound;85,000 per annum with a hybrid working pattern.
 
 
 
 
 ICAEW is evolving the way it manages and uses data by modernising how data is ingested, modelled and shared across the organisation. This work will help improve analytics, reporting and decision-making across a wide range of business areas.
 
 
 
 
 
 
 As Data Engineering Lead, you will provide technical leadership for ICAEW's data engineering capability, combining hands-on engineering with the leadership of a small team of two data engineers. Working closely with the Data Analytics &amp; Reporting team, Data Architect, Data Governance Manager and wider Technology colleagues, you will establish and embed best practice in data engineering, ensuring data platforms, pipelines and models are secure, scalable and fit for purpose.
 
 
 
 
 What you will be doing:
 
 
 
 
 
 
 <ul>
 <li>
 Lead, mentor and develop the data engineering team, ensuring delivery of robust, reliable pipelines and platforms
 </li>
 <li>
 Manage delivery of data engineering projects, ensuring timely outputs aligned to ICAEW's transformation programme
 </li>
 <li>
 Define and enforce engineering standards, patterns and best practices across the team
 </li>
 <li>
 Design and implement scalable data architectures including data lakes, warehouses and integration layers
 </li>
 <li>
 Ensure data is accessible, well-structured and compliant with governance, quality and security requirements
 </li>
 <li>
 Work as a peer to the Data Analytics &amp; Insights Lead, ensuring engineering and analytics capability develop in step with each other
 </li>
 </ul>
 <li>
 </li>
 
 
 
 
 
 
 
 
 
 
 What you will need:
 
 
 
 
 
 
 <ul>
 <li>
 Substantial experience in data engineering, with a previous role in a lead or senior engineering capacity
 </li>
 <li>
 Proven track record leading data engineering delivery within a Microsoft-first environment - Azure Data Factory, Databricks, Synapse, Azure Data Lake
 </li>
 <li>
 Hands-on experience building and managing ETL/ELT pipelines, data lakes and data warehouses in a cloud environment
 </li>
 <li>
 Proficient in Python and SQL, familiarity with Scala or R advantageous
 </li>
 <li>
 Experience with data orchestration and workflow management tooling such as Azure Data Factory or Airflow
 </li>
 </ul>
 
 
 
 Please see full job description attached.
 
 
 
 What you can expect from us
 
 
 
 We believe doing your best work starts with feeling supported both professionally and personally. That's why we offer a range of benefits designed to give you flexibility, security, and peace of mind:
 
 <ul>
 <li>
 A generous 9% pension
 </li>
 <li>
 Healthcare cash plan
 </li>
 <li>
 Private Medical Insurance
 </li>
 <li>
 Life assurance at 4x your salary
 </li>
 <li>
 24 days' holiday, and the option to buy or sell extra days
 </li>
 <li>
 Hybrid working
 </li>
 <li>
 Everyday savings through gym discounts, travel loans, and retail perks
 </li>
 <li>
 Enhanced family leave, including up to 6 months on full pay
 </li>
 <li>
 Ongoing wellbeing support, including access to CABA
 </li>
 <li>
 A paid day each year to volunteer for a cause that matters to you
 </li>
 </ul>
 
 It's all part of creating an environment where you can thrive, in work and beyond. For a full overview of our benefits package please see below.
 
 
 
 Additional Information
 
 
 
 
 
 
 
 
 Benefits
 
 
 
 
 Who We Are
 
 
 
 
 Diversity &amp; Inclusion
 
 
 
 
 Governance
 
 
 
 
 
 
 
 
 We reserve the right to close this vacancy earlier than the advertised closing date if we receive enough suitable applications. We therefore encourage interested candidates to apply as soon as possible to avoid disappointment.
 
 
 
 
</div>

Data Engineering Lead

Cognite - AI for Industry

<div><div><h1 class="jobSectionHeader">What Cognite is: Relentless to achieve</h1>

Cognite operates at the forefront of industrial digitalization, building AI, and data solutions that solve the world's hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements.

We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you'll feel right at home here.

Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.</div><div> 
<h1 class="jobSectionHeader">
About the role</h1>

Cognite's European Delivery organisation is growing in both scale and technical ambition. As Manager of Data Engineering, you will lead, develop, and retain a team of Data Engineers operating across some of the most complex industrial AI engagements in the market &mdash; ensuring they are technically strong, professionally developed, and set up to deliver at the standard our customers expect.

This is not a pure people management role. You will carry a meaningful delivery commitment &mdash; approximately 50% billable capacity &mdash; working on customer projects alongside your team. Your credibility as a manager depends on being a practitioner: you need to understand the work at depth in order to coach it, assess it, and 
raise the bar on it.

You will be part of the Value Delivery (VD) Europe profession leadership team and work closely with Portfolio Managers, and cross-functional leaders across Value Delivery.
<h1 class="jobSectionHeader">
How you'll demonstrate Ownership</h1>
</div>
<div>People Development &amp; Performance
<ul>
<li>Own the professional development of your Data Engineers spanning junior to seasoned Data Engineers, running structured 1:1s, calibrated performance reviews, and growth conversations grounded in the Data</li>
<li>Engineering framework.</li>
<li>Provide technically specific coaching &mdash; not generic management feedback. Your engineers should leave conversations with a clearer understanding of what good looks like and how to close the gap.</li>
<li>Identify and address skill gaps proactively, before they surface as delivery problems on customer engagements.</li>
<li>Build a team culture of ownership, technical rigour, and AI-first delivery &mdash; consistent with the behaviours defined in the EDF at every level</li>
</ul>
Delivery Accountability
<ul>
<li>Carry approximately 50% billable capacity on customer projects &mdash; this is a firm expectation, not a stretch target.</li>
<li>Review Statements of Work (SOWs) before your engineers are committed to them: assess scope fit, identify skill gaps, and flag delivery risks before they become customer problems.</li>
<li>Champion your team's interests in staffing decisions &mdash; actively match engineers to engagements where they can deliver and grow, not just where there is a vacancy to fill.</li>
<li>Act as an escalation point when delivery gets hard. Be the steady hand, not a passive observer.</li>
</ul>
Profession &amp; Team Building
<ul>
<li>Contribute to standardising delivery practices across Value Delivery Europe &mdash; drive consistency in how we design, build, and document data engineering solutions.</li>
<li>Organise and lead professional development sessions relevant to the team's growth areas and project landscape.</li>
<li>Collect, prioritise, and escalate product feedback from your engineers and customers to the relevant internal teams.</li>
<li>Lead or contribute to hiring for your team &mdash; define what good looks like for each level and bring that standard to interviews and onboarding.</li>
</ul>
AI-First Leadership
<ul>
<li>Model and reinforce AI-first delivery practices across your team &mdash; not as a policy, but as a standard of work.</li>
<li>Hold your engineers to the AI fluency expectations defined in the EDF at each level: daily use, responsible validation, knowledge sharing, and continuous upskilling.</li>
<li>Stay current on AI tooling relevant to industrial data engineering and bring that knowledge into your team's practice.</li>
</ul>
</div><div></div><div><div><h1 class="jobSectionHeader">What we are looking for</h1>

Required
<ul>
<li>Proven hands-on data engineering background &mdash; production-grade Python, SQL, REST APIs, and cloud-native pipeline development. You must be able to review your team's technical work and have credible technical conversations with customers.</li>
<li>At least one year of meaningful association with Cognite's platform and products, with direct delivery experience on customer engagements. External candidates will not be considered for this role.</li>
<li>Demonstrated ability to develop people &mdash; not just manage tasks. You have concrete examples of engineers you have helped grow, with specific feedback that changed how they work.</li>
<li>Customer-facing confidence: you can hold a technical conversation with a senior customer stakeholder, translate engineering decisions into business language, and manage expectations under pressure.</li>
<li>Strong operational judgement: you can read a SOW, spot a risk, and have a difficult conversation about scope before it becomes a delivery failure.</li>
</ul>
</div></div><div>Strong Advantage
<ul>
<li>Formal or informal people management experience &mdash; direct reports, tech leads, or structured mentorship programmes.</li>
<li>Deep Cognite Data Fusion expertise: data modelling, SDK development, AI/LLM integration patterns, deployment packs.</li>
<li>Experience working in a Value Delivery or professional services environment where you were simultaneously accountable for delivery and team capability. Familiarity with Cognite's EDF and comfort using it as a coaching and calibration tool.</li>
<li>Track record of hiring &mdash; you know what good looks like at different levels and you can articulate it clearly.</li>
</ul><h2 class="jobSectionHeader">How We Think About This Role</h2>

The best candidate for this role is an E3 Data Engineer &mdash; or equivalent &mdash; who is ready to multiply their impact through others, not just through their own technical output. Technical credibility is the foundation. A manager who cannot assess their engineers' work, engage meaningfully in SOW reviews, or hold a technical conversation with a customer will lose the team's respect quickly in this environment. People development is the job. The 50% delivery commitment is real &mdash; but the primary measure of success in this role is whether the Data Engineers on your team are growing, performing, and staying.
<h2 class="jobSectionHeader">
Why This Role</h2>
<ul><li>High visibility within Cognite's European Delivery organisation &mdash; direct line to VP Delivery Europe.</li>
<li>Meaningful influence over how Data Engineering is practised and developed across VD Europe.</li>
<li>Ownership of a team at a critical growth stage &mdash; the work you do here shapes the function for years.</li>
<li>Part of a profession leadership team that is actively building something, not maintaining the status quo.</li></ul></div><div></div><div> 
<div></div>
<div></div><div>Learn more about us</div> 
<div></div>
<div></div>
<div></div><div><ul><li>Impact 2025</li>
<li>Cognite's Industrial AI: Moonshot</li>
<li>We're globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.</li></ul> 
</div>
<div></div><div>Equal Opportunity</div>
<div>Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment.</div></div></div>

Data Engineering Leader

Booz Allen Hamilton

<div>AL/ML Technical Lead
The Opportunity:
As an AI/ML Technical Lead, you will define and drive the technical direction of mission-critical AI systems supporting Public Health and Safety. You will lead the design and implementation of scalable machine learning and generative AI solutions, including AI agents, retrieval-augmented generation (RAG) systems, and evaluation frameworks that ensure reliability, safety, and mission alignment.
You will serve as a technical authority across a community of engineers, partnering with data engineers, architects, product owners, and stakeholders to shape enterprise AI strategy, architecture standards, and delivery practices. Your work will span end-to-end AI system design&mdash;from data pipelines and modeling to agent orchestration, memory or state management, retrieval optimization, and production deployment.
In this role, you will also lead the development of AI evaluation strategies, prompt engineering workflows, and governance frameworks using tools such as MCP, Prompt Buddy, Codex, Claude, and OpenEvidence. You will ensure systems are performant, explainable, secure, and compliant with public health data privacy and ethical standards. This position is located in Atlanta, GA.
What You'll Work On:<ul><li>
Lead the design and development of scalable AI/ML systems using PySpark and Palantir Foundry for public health mission applications.</li><li>
Architect generative AI solutions, including AI agents, MCP-enabled workflows, Prompt Buddy integrations, and RAG pipelines.</li><li>
Define AI evaluation frameworks covering performance benchmarking, retrieval optimization, safety validation, and production monitoring.</li><li>
Design and implement agent architectures with memory, state management, orchestration, and structured or unstructured data interaction.</li><li>
Integrate advanced AI tools such as Codex, Claude, and OpenEvidence into enterprise AI workflows and platforms.</li><li>
Establish data governance, privacy, anonymization, documentation, and ethical AI standards across AI/ML systems.</li></ul>
Join us. The world can&rsquo;t wait.
You Have:<ul><li>
5+ years of experience designing, developing, and deploying machine learning and AI solutions using Python</li><li>
5+ years of experience with Generative AI, LLMs, AI agents, or RAG systems in enterprise environments</li><li>
3+ years of experience with AI agents, MCP frameworks, or AI evaluation strategy development</li><li>
Experience with ML frameworks such as TensorFlow or PyTorch for production model development</li><li>
Experience with data engineering using PySpark, SQL, and Palantir Foundry, including Foundry AIP</li><li>
Experience with MLOps platforms such as MLflow and cloud environments such as Azure or Databricks</li><li>
Experience integrating or working with AI tooling such as Codex, Claude, Prompt Buddy, or OpenEvidence in enterprise settings</li><li>
Knowledge of public health, healthcare, or government data systems and associated governance practices</li><li>
Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements</li><li>
Bachelor's degree in Computer Science, Engineering, or Data Science</li></ul>
Nice If You Have:<ul><li>
Experience working in healthcare, biomedical, or government public health AI/ML environments</li><li>
Experience with open-source LLMs such as LLaMA, Gemma, or Phi</li><li>
Experience with conversational AI, chatbot systems, or full-stack AI application development</li><li>
Experience with containerization, CI/CD, orchestration, and production MLOps pipelines</li><li>
Experience with Agile delivery environments and tools such as JIRA</li><li>
Experience writing technical documentation and presenting AI/ML architecture decisions to a broad range of audiences</li><li>
Knowledge of enterprise AI governance, compliance, and ethical AI frameworks</li><li>
Knowledge of AI systems for retrieval, ranking, and decision-support in high-stakes environments</li><li>
Ability to collaborate effectively in matrixed, cross-functional organization and design and deploy scalable AI systems involving retrieval optimization, memory or state management, and agent orchestration</li><li>
Master's degree in Computer Science, Data Science, AI/ML, or a related field</li></ul>
Vetting:
Applicants selected will be subject to a government investigation and may need to meet eligibility requirements of the U.S. government client.
Compensation
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen&rsquo;s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual&rsquo;s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $128,700.00 to $292,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen&rsquo;s total compensation package for employees. This posting will close within 90 days from the Posting Date.
Identity Statement
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Candidate AI Usage Policy
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided.
Work Model 
Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.<ul><li>
Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.</li><li>
Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.</li><li>
Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.</li></ul>
Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.</div>

AL/ML Technical Lead

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