Work with Microsoft Fabric (Lakehouses, Warehouses, Dataflows, Pipelines), Azure (SQL, Data Lake, Data Factory), SQL, Python, and REST API/SFTP to build ETL/ELT solutions. You will deliver modern data integration solutions for a diverse portfolio of clients within RSM’s Global VAT Team.

You will work with AWS, dbt, Spark, SQL, Kafka, Flink, and Airflow on a modern event-driven lakehouse using medallion architecture. The team builds and evolves the next-generation data platform for FDJ UNITED’s Sportsbook product, powering analytics, trading insight, and machine learning across multiple brands and markets.

Work with TensorFlow or PyTorch, Python, and related computer vision/graphics libraries on neural network architectures (convolutional, recurrent, transformer, diffusion-based). The Visual Computing Group (VCG) researches and develops generative models for game rendering and live video streaming to advance state-of-the-art runtime efficiency, visual quality, and latency.

Lead a team of DataOps and MLOps engineers building and evolving the ML platform and data operations for FDJ United’s Sportsbook product. You’ll work with AWS, Terraform, CloudFormation, Spark, Kafka, and CI/CD pipelines to enable end-to-end machine learning workflows from research to production.

You will work with backend development, databases, APIs, cloud services and production machine learning systems. This is for a growing technology business. No specific project or team size is mentioned.

Cint★3.5 (13)· London

Use Python and SQL for statistical modeling and machine learning to optimize Cint's programmatic research marketplace (Cint Exchange). You will analyze marketplace dynamics, conduct A/B tests, and build predictive models to guide product and pricing strategy.

Hudson River Trading★4.5 (2)· London

You will use Python, C++, Pandas, NumPy, R, or MATLAB to build and maintain predictive trading models. As an algorithm developer, you research and test novel order execution and model training methods, applying

Hudson River Trading★4.5 (2)· London

You will work with Python, Pandas, NumPy, R, and MATLAB to apply statistical analysis and machine learning to vast quantities of market data. The team builds and maintains predictive trading models for the company's high-performance trading infrastructure. You will research, develop, and test order execution methods to increase trading efficiency.

You will use TensorFlow or PyTorch, Python, and CV/graphics libraries, plus 3D engines like Unreal or Unity, to develop generative models for graphics and video. As part of the Visual Computing Group, you'll design neural networks and ML for PlayStation's game rendering and live

FDJ UNITED★4.0 (96)· London

Build and maintain batch and streaming data pipelines on AWS using dbt, Spark, SQL, Kafka/Flink, and Airflow. Work on the Sportsbook Data Platform, a modern event-driven lakehouse transforming sportsbook data (bets, offers, etc.) into curated datasets for analytics, trading insight, and machine learning.

FDJ UNITED★4.0 (96)· London

You'll lead a DataOps/MLOps team building an ML platform and data operations capability for FDJ United's Sportsbook, working with AWS, Terraform/CloudFormation, Spark, Kafka, and CI/CD pipelines for end-to-end ML lifecycle management. The role involves establishing best practices, monitoring, and governance while collaborating with Data Engineering, ML Engineering, Quants, and Data Science teams to personalize customer experiences and manage risk.

Work with SQL, Python, and modern data technologies (dbt, Spark, containers, DevOps tooling, orchestration tools, Git, AI/ML). As a Solution Engineer for the UK market, you present Snowflake’s technology and vision, working hands-on with prospects to demonstrate value through demos, proof-of-concepts, and design.

Work with Microsoft Fabric, Databricks, Azure, Snowflake, Python, Spark, SQL/NoSQL, PowerShell, Azure Data Factory and Azure DevOps to build a Fabric Data Lakehouse for USS’s Investment Management business, implementing scalable ETL/ELT frameworks across a medallion architecture. Share knowledge across a small team of data engineers and collaborate with architects, business stakeholders and data teams.

Work with Python, DevOps, Linux, Pandas, NumPy, and relational/non-relational databases. You'll join the Vendor Data Group at a global quant firm to build and maintain mission-critical ETL/ELT data integration pipelines and tools for alternative commodity and macro data, collaborating with a trading team.

Quant developer role building equity portfolio analytics frameworks, including MSCI Barra factor risk models, using Python and/or R, SQL, and big data tools like Apache Spark. You'll design infrastructure for data extraction and transformation, liaising with portfolio managers and traders at a global hedge fund. Requires 5+ years' buy-side or sell-side experience.

You'll work with Python (Pandas, Arrow, Spark) and potentially Lakehouse tools (Delta Lake, Databricks, AWS). Joining Global Data Engineering, you provide first-line support for a high-frequency trading market maker, ensuring robust data pipelines and APIs for trading teams.

Develop end-to-end solutions in Python (pandas/numpy) for the Commodities RAD team, building data, research & trading platforms and tools for Portfolio Managers. Also use React/Angular for web frontends and SQL for databases.

Backend engineers will use Java, Elixir, Scala, and TypeScript, plus Kubernetes, Docker, Linux, git, SQL, and Spark, to build compliance technology for a tech-driven proprietary trading firm. The team supports regulatory reporting and trade surveillance across global markets, with developers owning tasks end-to-end.

Working with Core Python, Pandas, NumPy, Flask/FastAPI/Django, SQL, and Unix/Linux, you will build backend applications and data processing pipelines for trading and risk management within an investment banking environment, supporting risk calculations, trade workflows, and reporting.

Build and maintain scalable data pipelines (BigQuery, Cloud Composer, Airflow, Dataflow, Spark, Flink, dbt) on GCP for AI/ML experiences. Use Docker/k8s, Terraform, SQL, shell scripting, and monitoring tools (Monte Carlo, Looker, NewRelic, Splunk). Collaborate with product engineering, data science, and business teams.

SmartestEnergy★4.1 (15)· London

You will work with Microsoft Azure Data Platform, Microsoft Fabric, PySpark, SQL, Python, Azure DevOps, CI/CD, and Power BI to build enterprise-scale data pipelines and Data Lakehouse/Data Warehouse solutions. The team develops modern cloud-based data solutions supporting ETL/ELT processes across Medallion Architecture for SmartestEnergy’s energy digitisation and decarbonisation goals.

Imperial College London★4.2 (238)· London

Work with PyTorch, GPU kernel programming, Verilog, and NVIDIA's Holoscan Sensor Bridge to integrate FPGA-based AI-ISP accelerators with edge GPU platforms. The project targets low-latency, high-throughput sensor-to-GPU streaming for the "Sense-Decide" pipeline in edge automation.

HCLTech★3.7 (11k)· London

Work with Quantexa Decision Intelligence Platform, Spark/PySpark, Python/Scala, and Big Data (Hive/HDFS) to design and develop data ingestion, transformation, and ETL pipelines. Focus on entity resolution, network analytics, and graph-based data models for analytics use cases.

bp★3.8 (5.2k)· London

Work hands-on with Python, SQL, C#, Spark/PySpark, Palantir Foundry, planlogic EOS ETRM, Stonebranch, and AWS to build and maintain backend services, RESTful APIs, front-end components, and data pipelines. You will support bp’s LNG Energy Trading and Risk Management applications, including bespoke solutions for trade capture, operations, analytics, risk, reporting, and finance. This Staff Engineer role also includes mentoring and line management of the engineering team.

You'll build scalable data infrastructure for AI applications using Python, SQL, Spark, dbt, LangChain, and cloud platforms (AWS, Azure, GCP), working with data pipelines, embeddings, vector search, and LLMOps tools like Terraform and LangSmith. QuantumBlack, McKinsey's AI division, develops next-generation AI systems and enterprise solutions for clients across industries. You'll collaborate with Data Scientists and ML Engineers in cross-functional Agile teams based in London, supporting the firm's AI adoption and complex problem-solving at scale.