machine learning engineer

ai engineer

data engineer

data scientist

senior software engineer

data pipeline engineer

Xcelirate

<div>Who Are We?
Xcelirate develops technologically-advanced platforms which are accessed by thousands of users every minute! We are proud to offer a workplace where the sharpest developers come together to strategically plan and swiftly execute practices which see us maintain our existing market dominance and attain global expansion. We owe our success to our customers who have seen us grow across a decade, and our talented team who have made that growth possible.
Who are we looking for?
The Data Engineer will focus on designing, developing, and maintaining robust data infrastructure to support use cases such as fraud detection but also general data engineering. The position emphasizes building scalable, high-performing data pipelines and storage systems for fraud use cases. Although this role involves light integration with machine learning models, its primary responsibility is creating the technical foundation that powers such use cases of fraud detection, analytics and reporting.
What will you be doing?<ul><li>
Develop and Maintain Pipelines: build and maintain efficient, scalable data pipelines for use cases such as fraud detection</li><li>
Support Fraud Analytics: enable analysts and product teams to identify and address emerging fraud patterns through engineered datasets</li><li>
Integrate Detection Models: collaborate with teams to operationalise external fraud detection models and integrate them into the data infrastructure</li><li>
Data Storage Optimisation: design and optimise data storage solutions for analysing fraud signals and managing historical data</li><li>
Feature Engineering: create fraud-specific datasets and features to enhance detection accuracy while supporting business and analytics teams</li><li>
Pipeline Monitoring and Optimisation: monitor fraud data pipelines to ensure system reliability and troubleshoot performance issues</li><li>
Best Practices Documentation: establish and document best practices for fraud-related data engineering</li><li>
Cross-Team Collaboration: partner with data, product, and engineering teams to proactively address fraud trends</li></ul> 

Requirements 
What will you bring along?<ul><li>
3+ years of experience as a data engineer with some expertise in fraud detection systems or similar</li><li>
Proficiency in Python and SQL, with knowledge of orchestration and transformation tools (e.g., Apache Airflow, DBT)</li><li>
Strong knowledge of database design, query optimisation, and ETL/ELT workflows, ideally including relational databases such as MySQL and columnar databases such as ClickHouse</li><li>
Experience building and maintaining production data pipelines</li><li>
Familiarity with leveraging machine learning models, primarily as a component of the broader data pipeline, including model registries such as MLflow</li><li>
Understanding of CI/CD processes for data pipelines</li><li>
Hands-on experience with data visualization platforms for trend reporting (e.g., Tableau, Superset, Metabase)</li><li>
Ability to support with model serving components, such as Flask or FastAPI</li><li>
Familiarity with statistical techniques for fraud trend analysis and reporting</li><li>
Experience with Git and version control in collaborative workflows</li></ul>
We are always looking for the best candidates, so if you think you would be a good fit even if you don't meet 100% of the requirements, we would love to hear from you! 
Benefits 
How We Support Our Contractors:<ul><li>
An annualised rate of &euro;98,000 + retention bonus scheme: Competitive contractor rate with a generous bonus paid every 6 months based on your time with us.</li><li>
Top-Notch Workstation: We provide the latest MacBook, branded merchandise, and everything you need for an optimal work environment.
</li><li>Global Co-Working Access: Work from a global network of co-working spaces to keep your work-life dynamic and flexible.
</li><li>Flexibility: Enjoy full flexibility in work location and hours, supporting a work-life balance tailored to your needs.
</li><li>Events and Gatherings: Participate in exciting events throughout the year, including team gatherings, cultural events, and other fun activities.</li></ul>
At Xcelirate, we&rsquo;re committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We pride ourselves in being an equal opportunity workplace.</div>

Data Engineer, Fraud

RocketFin Consulting

<div><h1 class="jobSectionHeader">Key Responsibilities</h1>

Data Engineering &amp; Pipelines
<ul>
<li>Build and enhance data pipelines and automation workflows on Azure for large-scale data ingestion and processing</li>
<li>Maintain and optimise existing toolkits, databases, dashboards, and API-based solutions</li>
<li>Develop end-to-end automated data solutions across commodities (Gas/LNG, Power, Weather)</li>
<li>Design and implement web scraping pipelines (e.g. US LNG data)</li>
</ul>
Analytics &amp; Real-Time Systems
<ul>
<li>Support real-time and batch analytics use cases in a dynamic trading environment</li>
<li>Enable and support analytics dashboards and GUI tools with reliable backend infrastructure</li>
<li>Develop real-time flow tracking solutions at critical market points</li>
</ul>
Data Modelling &amp; Architecture
<ul>
<li>Design and manage Snowflake data models, ensuring performance and scalability</li>
<li>Integrate external data sources including ENTSOE, EPEX, KPLER, MetDesk, WoodMac, and others</li>
<li>Collaborate with analytics teams to structure data for models and advanced analytics use cases</li>
<li>Build and maintain data platforms supporting ML models such as demand forecasting</li>
</ul>
Key Deliverables
<ul>
<li>LNG Sendout Optimisation Model</li>
<li>Ship Tracking &amp; Flow Monitoring Tools</li>
<li>Prompt Price &amp; Forward Curve Bootstrapping</li>
<li>Web scraping pipelines for US LNG and other market data sources</li>
<li>Real-time flow tracking infrastructure at critical market points</li>
<li>Data platforms supporting ML and demand forecasting models</li>
</ul><h1 class="jobSectionHeader">Required Skills &amp; Experience</h1>

Technical
<ul>
<li>Strong proficiency in Python for data engineering and pipeline development</li>
<li>Hands-on experience with Azure data services (Data Factory, Databricks, Blob Storage, etc.)</li>
<li>Experience designing and optimising Snowflake data models</li>
<li>Proven track record building and maintaining data pipelines at scale</li>
<li>Experience integrating third-party market data APIs and external data feeds</li>
<li>Familiarity with SQL and data warehousing concepts</li>
</ul>
Domain
<ul>
<li>Experience or strong interest in energy, commodities, or financial markets data</li>
<li>Understanding of real-time and batch data processing patterns in a trading context</li>
<li>Familiarity with market data providers such as ENTSOE, EPEX, KPLER, or similar</li>
</ul><h1 class="jobSectionHeader">Nice to Have</h1>
<ul><li>Experience with web scraping frameworks (e.g. Scrapy, BeautifulSoup, Playwright)</li>
<li>Knowledge of LNG, Gas, or Power market fundamentals</li>
<li>Experience supporting ML model pipelines or demand forecasting workflows</li>
<li>Exposure to GUI/dashboard tools such as Grafana, Power BI, or Streamlit</li>
<li>Knowledge of ship tracking data or AIS feeds</li></ul></div>

Data Engineer

Goldman Sachs

<div>
 
 Job Description
 
 
 
 
 The Opportunity
 
 
 
 Join a team building the data foundations that support the firm&rsquo;s AI and analytics capabilities. This role sits within the engineering effort to develop a modern Lakehouse and AI data platform that enables reliable, well-governed and high-performing data use across the firm.
 
 
 At Goldman Sachs, engineering teams are positioned at the centre of the business, building scalable systems, solving complex technical problems and turning data into action. In data engineering roles, the emphasis is on designing, building and maintaining large-scale data platforms, delivering production pipelines, improving reliability and quality, and partnering closely with users of the platform.
 
 
 This is a delivery-focused role for engineers who want to build robust data assets in production, work with modern data technologies, and grow over time within the firm. You will contribute to the data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases , and may also help extend platform tooling where additional functionality is needed.
 
 
 
 Role Summary
 
 
 
 As a
 
 Data Engineer
 
 in the
 
 Lakehouse and AI Data Platform team,
 
 you will design, build, test and support data pipelines and curated datasets on the firm&rsquo;s modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose. Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated.
 
 
 The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of delivery approaches across a wider set of use cases.
 
 
 
 Key Responsibilities
 
 
 
 
 Pipeline Engineering
 
 
 
 <ul>
 <li>
 Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
 </li>
 <li>
 Refactor or modernise existing data flows where needed to improve reliability, performance and maintainability.
 </li>
 <li>
 Where needed, build reusable tooling to improve delivery, consistency and operational support.
 </li>
 <li>
 Ensure data pipelines are production-ready, well tested and operationally supportable.
 
 
 </li>
 </ul>
 
 Data Modelling and Curation
 
 
 
 <ul>
 <li>
 Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
 </li>
 <li>
 Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
 </li>
 <li>
 Work with consumers to shape data products that are usable, well documented and aligned to business needs.
 
 
 </li>
 </ul>
 
 Data Quality and Reconciliation
 
 
 
 <ul>
 <li>
 Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
 </li>
 <li>
 Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
 </li>
 <li>
 Contribute to clear standards for testing, monitoring and issue resolution.
 </li>
 <li>
 Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.
 
 
 </li>
 </ul>
 
 Delivery and Partnership
 
 
 
 <ul>
 <li>
 Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
 </li>
 <li>
 Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.
 </li>
 <li>
 For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.
 
 
 </li>
 </ul>
 
 Required
 
 
 
 
 Skills and Experience
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or master&rsquo;s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
 </li>
 <li>
 Strong hands-on programming experience in Python or Java.
 </li>
 <li>
 Good working knowledge of SQL, including troubleshooting, optimisation and data analysis.
 </li>
 <li>
 Ability to learn new tools, internal platforms and delivery workflows quickly.
 </li>
 <li>
 Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.
 
 
 </li>
 </ul>
 
 Data Engineering Capability
 
 
 
 <ul>
 <li>
 Understanding of temporal data modelling, including the handling of historical state and change over time.
 </li>
 <li>
 Knowledge of schema design, schema evolution and data compatibility considerations.
 </li>
 <li>
 Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
 </li>
 <li>
 Ability to make sensible design choices across normalised and denormalised models, and between natural and surrogate keys.
 </li>
 <li>
 Practical approach to data quality, reconciliation and root-cause analysis.
 </li>
 <li>
 Experience building or supporting production data pipelines in a collaborative engineering environment.
 </li>
 <li>
 Experience working with distributed data processing frameworks such as Apache Spark.
 </li>
 <li>
 Working knowledge of common data formats such as JSON, Avro and Parquet.
 
 
 </li>
 </ul>
 
 For More Experienced Candidates
 
 
 
 <ul>
 <li>
 Stronger ownership of technical design across multiple datasets or pipeline domains.
 </li>
 <li>
 Experience guiding implementation standards, code quality and engineering practices within a team.
 </li>
 <li>
 Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers.
 
 
 </li>
 </ul>
 
 Technology Environment
 
 
 
 The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.
 
 
 
 Examples Of Technologies In Scope Include
 
 
 
 <ul>
 <li>
 Data processing and logic: ANSI SQL, Apache Spark, Kafka
 </li>
 <li>
 Data formats: JSON, Avro, Parquet
 </li>
 <li>
 Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
 </li>
 <li>
 Engineering and deployment: CI/CD tooling, containerised or Kubernetes-based deployment approaches where relevant
 
 
 </li>
 </ul>
 You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.
 
 
 
 What We Are Looking For
 
 
 
 We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes. It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective.
 
 
 
 Stronger Candidates Will Typically Demonstrate
 
 
 
 <ul>
 <li>
 sound judgement in technical trade-offs
 </li>
 <li>
 attention to detail in data correctness and testing
 </li>
 <li>
 a clear and structured approach to problem solving
 </li>
 <li>
 willingness to work closely with stakeholders and partner teams
 </li>
 <li>
 an ability to identify when delivery problems would be better solved through reusable tooling or platform improvements
 </li>
 <li>
 an interest in developing long-term expertise within the firm
 
 
 </li>
 </ul>
 
 About Goldman Sachs
 
 
 
 At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
 
 
 We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
 
 
 We&rsquo;re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
 
 
 &copy; The Goldman Sachs Group, Inc., 2026. All rights reserved.
 
 
 Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
</div>

Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Analyst/Associate - London

Athsai

<div>
 
 **Employment Type:** Permanent
 
 
 **Salary:** Up to &pound;75k
 
 
 **Location:**
 
 
 in the Central London
 
 
 
 
 
 **Work Arrangement:** In-office only, five days a week.
 
 
 
 ---
 
 
 
 
 
 **Job Title:** Data Engineer
 
 
 
 
 
 **Experience Required:** 8 Years
 
 
 
 
 
 **Role Overview:**
 
 
 
 
 
 We are seeking a Data Engineer to design and implement scalable data solutions, including real-time and ETL/ELT data pipelines. Responsibilities include optimizing our data warehouse, creating Power BI reports, and developing AI-driven solutions using OpenAI APIs.
 
 
 
 
 
 **Key Responsibilities:**
 
 
 
 
 
 - Design and implement robust real-time data pipelines from diverse sources.
 
 
 - Maintain a high-performance data warehouse for reporting and analytics.
 
 
 - Develop insightful Power BI dashboards and reports for stakeholders.
 
 
 - Use Azure Data Factory, Azure Functions, and Logic Apps for data integration.
 
 
 - Employ Python and SQL for data transformation and analysis.
 
 
 - Leverage OpenAI APIs for AI/ML solutions.
 
 
 - Collaborate with teams to align data strategy with business goals.
 
 
 - Ensure data quality, consistency, and compliance.
 
 
 
 
 
 **Required Qualifications:**
 
 
 
 
 
 - 7-8 years in Data Engineering, Data Architecture, or related roles.
 
 
 - Experience with ETL/ELT pipelines and complex data ecosystems.
 
 
 - Proficiency in Python and strong SQL skills.
 
 
 - Familiarity wit
 
 h Azure services (Data Factory, Logic Apps, etc.).
 
 
 
 - Expertise in Power BI for data modeling and visualization.
 
 
 - Experience with OpenAI APIs or similar services.
 
 
 - Understanding of data warehousing and data governance.
 
 
 
 
 
 **Preferred Qualifications:**
 
 
 
 
 
 - Knowledge of DevOps practices and CI/CD pipelines.
 
 
 - Familiarity with cloud-native data platforms (e.g., Databricks).
 
 
 - Understanding of data privacy regulations (e.g., GDPR).
 
</div>

Data Engineer - Azure/ADF

TPXimpact

<div><h3 class="jobSectionHeader">About The Role</h3>

Job level: 10 
 
We&rsquo;re looking for a Lead Data Engineer to join our Data Engineering and Analytics practice. 

In this role, you will: 
<div></div>
<ul>
<li>Lead the design, development, management and optimisation of data pipelines to ensure efficient data flows, recognising and sharing opportunities to reuse data flows where possible.
</li><li>Coordinate teams and set best practices and standards when it comes to data engineering principles.
</li><li>Champion data engineering across projects and clients. 
</li></ul><div></div><div>Responsibilities
<ul>
<li>Lead by example, holding responsibilities for team culture, and how projects deliver the most impact and value to our clients.
</li><li>Be accountable for the strategic direction, delivery and growth of our work.
</li><li>Lead teams, strands of work and outcomes, owning commercial responsibilities.
</li><li>Hold and manage uncertainty and ambiguity on behalf of clients and our teams.
</li><li>Ensure teams and projects are inclusive through how you lead and manage others.
</li><li>Effectively own and hold the story of our work, ensuring we measure progress against client goals and our DT missions.
</li><li>Work with our teams to influence and own how we deliver more value to clients, working with time and budget constraints.
</li><li>Strategically plan the overall project and apply methods and approaches.
</li><li>Demonstrably share work with wider audiences.
</li><li>Elevate ideas through how you write, speak and present. 
</li></ul><div><div>Dimensions</div> 
<div>
</div><div><ul><li>Headcount: Typically leads a multidisciplinary team or multiple workstreams (team size 5&ndash;15)
</li></ul><ul><li>Resource complexity: Provides leadership across multiple workstreams or technical domains within a project or programme. Responsible for delivery coordination, prioritisation, and quality, often overseeing more junior leads or specialists.
</li><li>Problem-solving responsibility: Solves highly complex problems, balancing technical, user, business, and operational needs. Applies expert judgement to make decisions, manage risks, and guide teams through ambiguity.
</li><li>Change management requirements: Leads or co-leads significant change initiatives. Responsible for managing stakeholder expectations, supporting adoption, and embedding sustainable ways of working.
</li><li>Internal/External interactions: Acts as a trusted partner to client and internal stakeholders at multiple levels. Leads workshops, presentations, and stakeholder engagement to ensure buy-in, alignment, and delivery clarity.
</li><li>Strategic timeframe working towards: Works across mid- to long-term delivery cycles (6&ndash;12 months), ensuring that near-term work supports broader programme and client objectives.
</li></ul></div></div></div><h3 class="jobSectionHeader">About You</h3>
Professional knowledge and experience

Essential
<ul>
<li>Proven experience in data engineering, data integration and data modelling
</li><li>Expertise with cloud platforms (e.g. AWS, Azure, GCP)
</li><li>Expertise with modern cloud data platforms (e.g. Microsoft Fabric, Databricks)
</li><li>Expertise with multiple data analytics tools (e.g. Power BI)
</li><li>Deep understanding of data warehousing concepts, ETL/ELT pipelines and dimensional modelling
</li><li>Proficiency in advanced programming languages (Python/PySpark, SQL)
</li><li>Experience in data pipeline orchestration (e.g. Airflow, Data Factory)
</li><li>Familiarity with DevOps and CI/CD practices (Git, Azure DevOps etc)
</li><li>Ability to communicate technical concepts to both technical and non-technical audiences
</li><li>Proven experience in delivery of complex projects in a fast paced environment with tight deadlines 
</li></ul> 
Desirable
<ul>
<li>Advanced knowledge of data governance, data standards and best practices.
</li><li>Experience in a consultancy environment, demonstrating flexibility and adaptability to client needs.
</li><li>Experience defining and enforcing data engineering standards, patterns, and reusable frameworks
</li><li>Professional certifications in relevant technologies (e.g. Microsoft Azure Data Engineer, AWS Data Analytics, Databricks Certified Professional Data Engineer) 
</li></ul><div></div><div>Skills

Data Development Process
<ul>
<li>Design, build and test data products that are complex or large scale</li>
<li>Build and lead teams to complete data integration services integration and reusable pipelines that meet performance, quality and scalability standards</li>
<li>Collaborate with architects to align solutions with enterprise data strategy and target architectures</li>
</ul>
Data Engineering and Manipulation
<ul>
<li>Work with data analysts, engineers and data science and AI specialists to design and deliver products into the organisation effectively.</li>
<li>Understand the reasons for cleansing and preparing data before including it in data products and can put reusable processes and checks in place.</li>
<li>Access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation.</li>
<li>Optimise data pipelines and queries for performance and cost efficiency in distributed environments</li>
</ul>
Testing (Data)
<ul>
<li>Review requirements and specifications, and define system integration testing conditions for complex data products and support others to do the same</li>
<li>Identify and manage issues and risks associated with complex data products and support others to do the same</li>
<li>Analyse and report system test activities and results for complex data products and support others to do the same</li>
</ul>
Other Skills
<ul>
<li>Proficiency in developing and maintaining complex data models (conceptual, logical and physical).</li>
<li>Strong skills in data governance and metadata management.</li>
<li>Experience with data integration design and implementation.</li>
<li>Ability to write efficient, maintainable code for large scale data systems.</li>
<li>Experience with CI/CD pipelines, version control, and infrastructure-as-code (e.g. Git, Azure DevOps).</li>
<li>Strong stakeholder communication skills, with the ability to translate technical concepts into business terms.</li>
<li>Ability to mentor junior engineers, foster collaboration, and build a high-performing data engineering culture.</li>
</ul><div>Behaviours and PACT values
<ul><li>Purpose: Be values-driven, recognising that our client's needs are paramount. Approach client engagements with professionalism and creativity, balancing commercial and operational needs.</li>
<li>Accountability: Be accountable for delivering your part of a project on time and under budget and working well with other leaders. Lead by example, promoting a culture where quality and client experience are foremost.</li>
<li>Craft: Balance multiple priorities while leading high-performing teams. Navigate ambiguity and set the technical direction and approach to support positive outcomes.</li>
<li>Togetherness: Collaborate effectively with others across TPXimpact. Build strong relationships with colleagues and clients.</li></ul><div> 
</div>
 
</div></div><h3 class="jobSectionHeader">About Us</h3>
<div>People-Powered Transformation</div> 
<div></div>
<div>
We're a purpose driven organisation, supporting organisations to build a better future for people, places and the planet. Combining vast experience in the public, private and third sectors and expertise in human-centred design, data, experience and technology, we&rsquo;re creating sustainable solutions ready for an ever-evolving world. 


At the heart of TPXimpact, we&rsquo;re collaborative and empathetic. We&rsquo;re a team of passionate people who care deeply about the work we do and the impact we have in the world. We know that change happens through people, with people and for people. That&rsquo;s why we believe in people-powered transformation. 


Working in close collaboration with our clients, we seek to understand their unique challenges, questioning assumptions and building in their teams the capabilities and confidence to continue learning, iterating and adapting. 


Benefits Include:
<ul>
<li>30 days holiday + bank holidays</li>
<li>2 volunteer days for causes that you are passionate about</li>
<li>Maternity/paternity - 6 months Maternity Leave, 3 months Paternity Leave</li>
<li>Life assurance</li>
<li>Employer pension contribution of 5%</li>
<li>Health cash plan</li>
<li>Personal learning and development budget</li>
<li>Employee Assistance Programme</li>
<li>Green incentive programmes including Electric Vehicle Leasing and the Cycle to Work Scheme</li>
<li>Health assessments</li>
</ul>
</div><div>About TPXimpact - Digital Transformation 

We drive fundamental change in approaches to product and service development, delivery and technology. Our agile, multidisciplinary teams use technology, design and data to deliver better results, improving outcomes for individuals, organisations and communities. 
By working in the open, in partnership with our clients, we not only transform their systems and services but also build the capability of their teams, so work can continue without us in the longer term. Our focus is sustainable change, always delivered with positive impact. 


We&rsquo;re an inclusive employer, and we care about diversity in our teams. Let us know in your application if you have accessibility requirements during the interview.</div></div>

Lead Data Engineer

Claims Recruitment Services Limited

<div><div><h3 class="jobSectionHeader">Job Information</h3>
<ul><div>Date Opened
<div>23/06/2026
</div></div><div>Job Type
<div>Permanent
</div></div><div>Industry
<div>Insurance
</div></div><div>City
<div>London
</div></div><div>Province
<div>London
</div></div><div>Country
<div>United Kingdom
</div></div><div>Postal Code
<div>N/A
</div></div></ul></div><div><h3 class="jobSectionHeader">Job Description</h3>
<div>We are recruiting for a Data Engineer on behalf of one of the UK's fastest-growing technology consultancies, ranked in the Financial Times FT1000 Europe's Fastest-Growing Companies list in both 2025 and 2026. 
The successful candidate will contribute to the mission of protecting the UK through data-driven insights and solutions, working closely with public sector clients and project teams to develop and implement data engineering pipelines that support data science and business intelligence use cases. 
Key Responsibilities 
<ul><li>
Assessing clients' technical needs and managing stakeholder relationships appropriately. 
</li><li>
Identifying, designing, and implementing ETL / ELT pipelines across on-premise, cloud-based, and hybrid environments, including harvesting, ingesting, integrating, cleansing, migrating, and converting structured and unstructured data. 
</li><li>
Designing and managing data structures, storage, and data flows to enable analytics, machine learning, business intelligence, compliance, and system integration. 
</li><li>
Building solutions with security, compliance, scalability, reliability, and portability at their core, ensuring all data handling aligns with ethical principles and relevant legislation.</li></ul> 
Key Requirements 
<ul><li>
3+ years of degree level industry experience in data engineering, preferably in a consultancy or client delivery setting. 
</li><li>
Proven experience across data analysis, extraction, transformation, and loading, with hands-on knowledge of relevant technologies such as Spark, Python, Kafka, SQL, Airflow, and cloud-based ETL services. 
</li><li>
Experience designing schemas for data warehouses and data marts, assessing data quality issues, and integrating quality checks into pipelines. 
</li><li>
Knowledge of cloud infrastructure (AWS, Azure, or GCP) and database technologies including relational and non-relational stores such as Postgres, Hadoop, Elasticsearch, and Graph databases. 
</li><li>
Solid understanding of coding best practices, design patterns, CI / CD tooling, versioning, dependency management, error handling, monitoring, and alerting.</li></ul> 
If you want to apply your data engineering skills to work that genuinely matters, at a business with the momentum to match, get in touch today. 
Due to the nature of the clients and projects supported by this role, all candidates must be British nationals and eligible to obtain UK Security Clearance (SC). Candidates holding dual nationality or multiple passports are unfortunately unable to be considered for this position.<div></div><div> 
We do not retain any personal information without your permission. Please refer to our data protection policy on our official website for more information.</div></div></div></div>

duvo.ai

<div>
 
 About The Role
 
 
 
 We already collect a lot of data &mdash; now we need someone to turn it into leverage. This is not a classic pipelines-and-warehouse hire (that's table stakes). It's an AI-native take on the data role: build the foundations, then build the agents and internal workflows on top that make the whole organization faster and more data-driven.
 
 
 What you'll own
 
 
 The data foundation &mdash; pipelines, warehouse, the substrate. Necessary, but not the point.
 
 
 Agents and internal workflows built on top of that foundation &mdash; the actual leverage.
 
 
 Enabling the org (product and beyond) to make faster, data-driven decisions.
 
 
 This is an AI-native, senior role for someone who builds. You'll work across the data substrate and the agentic layer on top of it, and you'll be measured by how much faster and smarter you make the rest of the team.
 
 
 Compensation Range: &euro;135K - &euro;190K
 
 
</div>

Data Engineer (EU/UK Based - Remote)

Explore 276+ Data pipeline engineer in London this July. Find roles across top companies and apply now to boost your career.

276 data pipeline engineer jobs In London

Data Engineer, Fraud

Data Engineer

Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Analyst/Associate - London

Data Engineer - Azure/ADF

Lead Data Engineer

Data Engineer

Data Engineer (EU/UK Based - Remote)