Top Data Engineer Jobs in London

The Senior Software Engineer II will design and implement generative AI services, drive architectural standards, mentor engineers, and collaborate across teams to enhance data solutions.

Develop and scale the data platform: build time-series data pipelines, APIs, databases, CI/CD and cloud/on-prem infrastructure; apply AI for automated analysis; ensure data quality, testing and cross-team collaboration.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

As a Senior Data Engineer, you will design and develop large-scale data processing software, manage technical components, and mentor team members, while ensuring code quality and operational readiness.

As a Senior Data Engineer, you'll design and develop data processing solutions, collaborate with architects, mentor team members, and ensure operational readiness in agile teams.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

As a Software Engineer in Data and Lifecycle, you'll design and implement solutions for trade automations, maintain reference data, and collaborate with stakeholders.

Seeking a Software Engineer for the Data and Lifecycle team to automate trade processes, maintain reference data, and collaborate with stakeholders on derivatives.

The Data Centre Engineer will manage data centre operations including design, installation, and maintenance of equipment while ensuring data safety and accessibility.

Customer-embedded Principal Data Scientist who leads account-aligned delivery: translate business needs into technical approaches, build and ship production-grade solutions, influence senior stakeholders, prepare transitions to FDE crews, and operate with speed and entrepreneurial ownership to drive sustained business outcomes.

Design, build and optimise scalable cloud-native data platforms and pipelines using Microsoft Fabric, Azure Databricks, Synapse and ADF. Implement ETL/ELT with PySpark and SQL, ensure data governance, performance, reliability, and collaborate with data scientists and engineers to deliver production solutions.

The Forward Deployed Data Solution Engineer works embedded within teams to ensure software quality, guide technical decisions, and design integration patterns, focusing on AI-enabled capabilities and data governance.

Build and operate production data pipelines and streaming integrations ingesting market data and asset telemetry. Partner with trading and data science teams to deliver data for forecasting models. Work with stakeholders to build integrations and analytical views and improve the data platform powering GAIA across the business.

Collaborate with traders and researchers to design and build robust data systems for trading analysis in a fast-paced environment.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

Design, build, and maintain scalable cloud-based data platform and ETL pipelines on Azure. Develop data models, ensure data quality, optimize performance, implement governance and security, and collaborate with stakeholders and data scientists to enable data-driven solutions.

Translate business needs into technical solutions by collecting, cleaning, and analysing data from multiple sources; build and maintain dashboards and reports; use SQL and Python to automate data processes; ensure data quality; present findings to stakeholders and collaborate across teams to drive process improvements.

The Data Platform Engineer will scale and modernize the data lakehouse, build data products, write ETL processes, and support analytics across the business.

Build and maintain large-scale data pipelines and distributed systems to ingest, process, store, and serve petabytes of vehicle data for ML and AD/ADAS development. Collaborate with ML and CV engineers, design cloud-native solutions, and ensure reliability, observability, and automation across data platforms.

Design, build, and operate scalable, reliable data lake and catalog services. Lead technical design, mentor engineers, promote observability/SLOs, and collaborate cross-functionally to deliver production-grade data platform capabilities.

Build and evolve a data and ML platform: implement IaC and CI/CD, develop Python/Spark components, support data/ML lifecycle and AgentOps, enforce security and observability, and collaborate with engineers and scientists to enable platform adoption.

The Staff Backend Engineer will lead the technical vision for Depop's data platform, focusing on data ingestion, scalability, observability, and architecting data solutions while mentoring team members.

Design and develop scalable reference-data services, automate lifecycle events, build/internal and external reporting (real-time and batch), collaborate with research and execution teams, maintain CI/CD and automated testing, and integrate Python and C# components to support trading and research workflows.

Develop scalable cloud-native platforms for real-time tick data. Collaborate with researchers, architect systems, and build distributed data architectures.

Build and deliver internal data tools and platforms, support ingestion systems and promote operational excellence in a collaborative environment.