machine learning engineer

ai engineer

data engineer

data scientist

senior software engineer

big data engineer

Xcelirate

<div><p><b>Who Are We?</b></p><p>
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.</p><p><b>
Who are we looking for?</b></p><p>
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.</p><p><b>
What will you be doing?</b></p><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><br>
<b>
Requirements</b><p><b><br>
What will you bring along?</b></p><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><p>
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!</p><b><br>
Benefits</b><p><b><br>
How We Support Our Contractors:</b></p><ul><li>
<b>An annualised rate of &euro;98,000</b> + retention bonus scheme: Competitive contractor rate with a generous bonus paid every 6 months based on your time with us.</li><li><b>
Top-Notch Workstation</b>: We provide the latest MacBook, branded merchandise, and everything you need for an optimal work environment.
</li><li><b>Global Co-Working Access</b>: Work from a global network of co-working spaces to keep your work-life dynamic and flexible.
</li><li><b>Flexibility</b>: Enjoy full flexibility in work location and hours, supporting a work-life balance tailored to your needs.
</li><li><b>Events and Gatherings</b>: Participate in exciting events throughout the year, including team gatherings, cultural events, and other fun activities.</li></ul><p></p><p>
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.</p></div>

Data Engineer, Fraud

Claims Recruitment Services Limited

<div><div><h3 class="jobSectionHeader"><b>Job Information</b></h3>
<ul><div><p>Date Opened</p>
<div><b>23/06/2026</b><b>
</b></div></div><div><p>Job Type</p>
<div><b>Permanent</b><b>
</b></div></div><div><p>Industry</p>
<div><b>Insurance</b><b>
</b></div></div><div><p>City</p>
<div><b>London</b><b>
</b></div></div><div><p>Province</p>
<div><b>London</b><b>
</b></div></div><div><p>Country</p>
<div><b>United Kingdom</b><b>
</b></div></div><div><p>Postal Code</p>
<div><b>N/A</b><b>
</b></div></div></ul></div><div><h3 class="jobSectionHeader"><b>Job Description</b></h3>
<div><p>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.</p><p><br>
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.</p><p><b><br>
Key Responsibilities</b></p><br>
<ul><li>
Assessing clients' technical needs and managing stakeholder relationships appropriately.<br>
</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.<br>
</li><li>
Designing and managing data structures, storage, and data flows to enable analytics, machine learning, business intelligence, compliance, and system integration.<br>
</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><p><b><br>
Key Requirements</b></p><br>
<ul><li>
3+ years of degree level industry experience in data engineering, preferably in a consultancy or client delivery setting.<br>
</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.<br>
</li><li>
Experience designing schemas for data warehouses and data marts, assessing data quality issues, and integrating quality checks into pipelines.<br>
</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.<br>
</li><li>
Solid understanding of coding best practices, design patterns, CI / CD tooling, versioning, dependency management, error handling, monitoring, and alerting.</li></ul><p><br>
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.</p><p><br>
Due to the nature of the clients and projects supported by this role, all candidates must be British nationals and eligible to obtain <b>UK Security Clearance (SC)</b>. Candidates holding dual nationality or multiple passports are unfortunately unable to be considered for this position.</p><div></div><div><i><br>
We do not retain any personal information without your permission. Please refer to our data protection policy on our official website for more information.</i></div></div></div></div><p></p>

Data Engineer

HOMETRACK

<div><p>Hybrid working pattern - 2 days per week from London Bridge Office</p><p>
Hometrack is seeking an experienced Senior Data Engineer to join our product team to help us deliver Hometrack's Data Services ambitions. By building the best property data sets available, which back the market leading AVM, trusted by 17 out of the top 20 mortgage lenders in the UK, we open the door to helping all our customers make the decisions that matter to them - to buy, to lend, to build.</p><p></p><p>
The Data Services team works with 100s of customers across the property sector ranging from mortgage lenders, house builders, investors, fin &amp; proptech, insurers, telco, retail and utilities, as well as others involved in the housing market. Our mission is to inform decision makers by putting the UK&rsquo;s most trusted property data and insights at the heart of the property ecosystem.</p><p></p><p>
Our market intelligence and customer acquisition, retention and engagement solutions are relied upon by leading industry professionals.</p><p></p><p>
Data Services has a diverse product range covering market, property and mover intelligence, delivered through varying mechanisms including data platforms, reports and APIs, providing maximum flexibility when matching our customers&rsquo; needs to the most effective data solution.</p><p></p><p>
Hometrack is looking for an experienced senior data engineer to help us make this a reality.</p><p>
What we&rsquo;re looking for in a Senior Data Engineer:</p><ul><li>
Be accountable for team outcomes, playing your part in achieving team goals and ensuring your contribution positively impacts overall success</li><li>
Be a continual improver, looking for ways to help the team do better and be better, again and again</li><li>
Write maintainable, testable code and enjoy providing code reviews</li><li>
Be experienced with Databricks, Delta Lake and Lakehouse architecture for efficient data management; experience with ETL processes and optimizing data pipelines for performance</li><li>
Be strong in Python, SQL, and PySpark</li><li>
Be passionate about building data products for stakeholders and customers, ensuring they are stable, scalable, secure, accurate, observable, and performant</li><li>
Enjoy collaborating closely with colleagues in Product, Analytics, Security, and Software, explaining technical concepts to non-technical audiences</li></ul></div>

Senior Data Engineer

Macro Hive

<div>
 <p>
  <strong>
   Data Engineer &mdash; Markets &amp; AI Data Platform
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Macro Hive
  </strong>
  (BGC Group) is a leading independent provider of global macro strategy. We combine best-in-class human and artificial intelligence to provide institutional investors with market-leading investment opportunities
 </p>
 <p>
  .Our experts leverage large language models, such as those that power chatbots like ChatGPT, to develop innovative, data-driven solutions to complex financial problems and to build state-of-the-art models in search of alpha signals across asset classes
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Mission:
  </strong>
 </p>
 <p>
  Build the data backbone for AI, quant models and client tools.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Owns:
  </strong>
 </p>
 <ul>
  <li>
   Market, economic, research, news, internal and Fenics/BGC data pipelines
  </li>
  <li>
   Bronze/silver/gold data architecture
  </li>
  <li>
   Data quality gates
  </li>
  <li>
   Instrument, ticker, country, client, source and theme metadata
  </li>
  <li>
   APIs/tables for agents, dashboards and quant models
  </li>
  <li>
   Data lineage, entitlements, permissions and monitoring
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Must have:
  </strong>
 </p>
 <ul>
  <li>
   Strong Python and SQL
  </li>
  <li>
   Production ETL/ELT experience
  </li>
  <li>
   Cloud data stack: Azure, Databricks, Delta Lake, Snowflake or similar
  </li>
  <li>
   Time-series or complex-data experience
  </li>
  <li>
   Data quality, monitoring and audit awareness
  </li>
  <li>
   Ability to turn messy data into clean model-ready datasets
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Nice to have
  </strong>
 </p>
 <ul>
  <li>
   Experience with ticker level data using for example kdb+
  </li>
 </ul>
 <p>
  <br/>
 </p>
</div>


Capgemini

<div><div><div><div><p><b>About the Job you are considering:</b></p>
<p>We are looking for an experienced AWS Databricks Engineer with strong hands-on expertise in Databricks, AWS cloud services, PySpark, Spark SQL, Delta Lake, Python, SQL, and data engineering. The candidate will be responsible for designing, developing, optimizing, and supporting scalable data pipelines and Lakehouse solutions for a banking client.</p>
<p>The ideal candidate should have strong experience in building enterprise-grade data platforms, processing large volumes of structured and semi-structured data, and implementing secure, reliable, and high-performance data pipelines in AWS-based environments.</p>
<p><b>Hybrid working:</b></p>
<p>The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time.</p>
<p><b>Your Role:</b></p>
<ul><li>Define end-to-end data architecture for Azure-based data platforms using Azure Databricks, Azure Data Factory, ADLS Gen2, Delta Lake, Azure Synapse, and Power BI.</li>
<li>Design scalable and secure lakehouse architecture using bronze, silver, and gold data layers.</li>
<li>Lead architecture and design for data ingestion, transformation, curation, data marts, reporting, and analytics solutions.</li>
<li>Create high-level and low-level data architecture documents, data flow diagrams, integration architecture, and data platform blueprints.</li>
<li>Define architecture patterns for batch, incremental, real-time, and API-based data ingestion.</li>
<li>Design reusable data ingestion and transformation frameworks using ADF and Databricks.</li>
<li>Define data models for London Market Insurance data including policy, claims, premium, broker, bordereaux, delegated authority, reinsurance, exposure, and regulatory reporting data.</li>
<li>Work with business analysts and insurance SMEs to understand London Market business processes and translate requirements into data architecture.</li>
<li>Define standards for data modelling, source-to-target mapping, data quality, reconciliation, metadata, lineage, and auditability.</li>
<li>Design data governance, security, access control, and compliance frameworks for insurance data.</li>
<li>Support cloud migration, data warehouse modernisation, reporting transformation, and legacy system decommissioning initiatives.</li>
<li>Review technical designs, data models, ETL/ELT pipelines, and engineering implementation.</li>
<li>Provide architectural guidance to data engineers working on Azure Databricks, ADF, PySpark, SQL, and Delta Lake.</li>
<li>Collaborate with enterprise architecture, solution architecture, security, infrastructure, DevOps, and business teams.</li>
<li>Define CI/CD, DevOps, deployment, monitoring, and operational support architecture for data platforms.</li>
<li>Identify performance, scalability, reliability, and cost optimisation opportunities across Azure data services.</li>
<li>Support governance forums, architecture review boards, design authority meetings, and client stakeholder workshops.</li>
</ul><p><b>Your Skills:</b></p>
<ul><li>Strong hands-on experience with Databricks on AWS.</li>
<li>Strong experience with Apache Spark / PySpark.</li>
<li>Excellent programming skills in Python.</li>
<li>Strong SQL skills including complex queries, joins, CTEs, window functions, and query optimization.</li>
<li>AWS Secrets Manager &ndash; Secure secrets and credential management.</li>
<li>Amazon CloudWatch &ndash; Monitoring, logging, and alerting.</li>
<li>AWS Step Functions &ndash; Workflow orchestration, if applicable.</li>
<li>Amazon RDS / Aurora / Redshift &ndash; Source or target databases, where applicable.</li>
<li>Develop and maintain Databricks notebooks, workflows, jobs, and libraries.</li>
<li>Build reusable PySpark frameworks for ingestion, transformation, and data validation.</li>
<li>Implement Delta Lake features such as ACID transactions, schema evolution, time travel, and optimized storage.</li>
<li>Design and implement bronze, silver, and gold layers using medallion architecture.</li>
<li>Tune Databricks clusters for performance and cost optimization.</li>
<li>Monitor Databricks jobs and handle failures, retries, alerts, and job dependencies.</li>
<li>Implement job orchestration using Databricks Workflows, Airflow, AWS Step Functions, or similar tools.</li>
<li>Manage secrets, environment variables, and secure connections.</li>
<li>Support migration from legacy Hadoop/Spark platforms to Databricks on AWS, if required.</li>
</ul><p><b>'We are a Disability Confident Employer:</b></p>
<p>Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government&rsquo;s Disability Confident scheme. As part of our commitment to inclusive recruitment, we will offer an interview to all candidates who:</p>
<ul><li>Declare they have a disability, and</li>
<li>Meet the minimum essential criteria for the role.</li>
</ul><p>Please opt in during the application process.</p>
<p><b>Make It Real (what does it mean for you):</b></p>
<ul><li>You&rsquo;d be joining an accredited Great Place to work for Wellbeing in 2024. Employee wellbeing is vitally important to us as an organisation. We see a healthy and happy workforce a critical component for us to achieve our organisational ambitions.</li>
<li>To help support wellbeing we have trained &lsquo;Mental Health Champions&rsquo; across each of our business areas, and we have invested in wellbeing apps such as Thrive and Peppy.</li>
<li>You will be empowered to explore, innovate, and progress. You will benefit from Capgemini&rsquo;s &lsquo;learning for life&rsquo; mindset, meaning you will have countless training and development opportunities from thinktanks to hackathons, and access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard ManageMentor, Cybersecurity qualifications and much more.</li>
<li>You will be joining one of the World&rsquo;s Most Ethical Companies&reg;, as recognised by Ethisphere&reg; for 13 consecutive years. We live our values by making ethical business choices every day. Working ethically is at the centre of our culture at Capgemini, meaning you will be helping to create a future we can all be proud of.</li>
</ul><p><b>Why you should consider Capgemini:</b></p>
<p>Growing clients&rsquo; businesses while building a more sustainable, more inclusive future is a tough ask. When you join Capgemini, you&rsquo;ll join a thriving company and become part of a collective of free-thinkers, entrepreneurs and industry experts. We find new ways technology can help us reimagine what&rsquo;s possible. It&rsquo;s why, together, we seek out opportunities that will transform the world&rsquo;s leading businesses, and it&rsquo;s how you&rsquo;ll gain the experiences and connections you need to shape your future. By learning from each other every day, sharing knowledge, and always pushing yourself to do better, you&rsquo;ll build the skills you want. You&rsquo;ll use your skills to help our clients leverage technology to innovate and grow their business. So, it might not always be easy, but making the world a better place rarely is.</p>
<p><b>About Capgemini:</b></p>
<p>Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organisations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of &euro;22.1 billion.</p>
<p><b>Make it real | www.capgemini.com</b></p>
<div><div><div><h2 class="jobSectionHeader"><b>Job Description</b></h2>
</div><div>The role is responsible for creating visual representations of data, concepts, ideas, or designs to facilitate understanding, communication, and decision-making. The work also involves translating complex information into visual formats that are clear, engaging, and impactful.</div>
</div><div><div><h2 class="jobSectionHeader"><b>Job Description - Grade Specific</b></h2>
</div><div>The role leverages their expertise in visual communication and data visualization to help clients make informed decisions, enhance their data driven storytelling, and improve their overall visual communication strategies.</div></div></div></div></div></div></div>

AWS Databricks Engineer

Goldman Sachs

<div>
 <strong>
  Job Description
  <br/>
  <br/>
 </strong>
 <strong>
  The Opportunity
  <br/>
  <br/>
 </strong>
 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.
 <br/>
 <br/>
 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.
 <br/>
 <br/>
 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.
 <br/>
 <br/>
 <strong>
  Role Summary
  <br/>
  <br/>
 </strong>
 As a
 <strong>
  Data Engineer
 </strong>
 in the
 <strong>
  Lakehouse and AI Data Platform team,
 </strong>
 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.
 <br/>
 <br/>
 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.
 <br/>
 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <strong>
  Pipeline Engineering
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Data Modelling and Curation
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Data Quality and Reconciliation
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Delivery and Partnership
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required
  <br/>
  <br/>
 </strong>
 <strong>
  Skills and Experience
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Data Engineering Capability
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  For More Experienced Candidates
  <br/>
  <br/>
 </strong>
 <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.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Technology Environment
  <br/>
  <br/>
 </strong>
 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.
 <br/>
 <br/>
 <strong>
  Examples Of Technologies In Scope Include
  <br/>
  <br/>
 </strong>
 <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
   <br/>
   <br/>
  </li>
 </ul>
 You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.
 <br/>
 <br/>
 <strong>
  What We Are Looking For
  <br/>
  <br/>
 </strong>
 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.
 <br/>
 <br/>
 <strong>
  Stronger Candidates Will Typically Demonstrate
  <br/>
  <br/>
 </strong>
 <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
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  About Goldman Sachs
  <br/>
  <br/>
 </strong>
 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.
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 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
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</div>


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

RocketFin Consulting

<div><h1 class="jobSectionHeader"><b>Key Responsibilities</b></h1>
<p><b>
Data Engineering &amp; Pipelines</b></p>
<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>
<p><b>Analytics &amp; Real-Time Systems</b></p>
<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>
<p><b>Data Modelling &amp; Architecture</b></p>
<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>
<p><b>Key Deliverables</b></p>
<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"><b>Required Skills &amp; Experience</b></h1>
<p><b>
Technical</b></p>
<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>
<p><b>Domain</b></p>
<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"><b>Nice to Have</b></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><p></p>

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