Top Data Engineer Jobs in London

Design, build, and operate enterprise-grade, cloud-native data platforms and pipelines for Preqin's Company Intelligence. Provide technical architecture, end-to-end delivery, data governance, observability, and reliability while leading and mentoring a small team and partnering with product and analytics to deliver measurable business value.

Design, build, deploy, and optimize distributed big-data ingestion, standardization, and ML pipelines at scale. Support platform reliability, cost controls, security accreditations, and customer integrations. Serve as technical lead in client meetings, mentor engineers, and recommend tools and best practices for data pipeline development and deployment.

As a Senior Azure Data Engineer, you'll design and deploy data engineering pipelines using Databricks on Azure, collaborate with clients, and implement best practices in a consulting environment.

Lead architecture and delivery of data solutions on Databricks within Azure, ensuring best practices in data pipeline design and implementation.

The role involves designing and building data pipelines using AWS technologies, leading projects, and mentoring junior engineers in a data engineering context for financial services.

The Senior Data Platform Engineer will manage Databricks infrastructure, develop cloud systems, collaborate with teams, and enhance machine learning workflows.

Lead infrastructure engineering for critical market data platforms: provide production support, implement changes, lead architecture and automation workstreams, validate AI-assisted recommendations, ensure performance, resilience and data quality, and collaborate across platforms to modernize and stabilize trading market data infrastructure.

Design, build, test, and maintain scalable ELT data pipelines and architectures using Python and Databricks. Ensure data security, entitlement controls, and SDLC quality (CI/CD, testing). Use SQL/NoSQL, update data models, apply enterprise-authorized AI responsibly, and support controls and auditability across the data lifecycle.

Build and maintain ultra-high-scale data platforms and pipelines (petabyte scale). Write fault-tolerant Java/Spark code, design low-latency query capabilities, improve performance and stability, participate in technical reviews, and enable customers/internal teams to query and extract analytics from event data.

The Senior Data Scientist will unify technical vision, develop and deploy ML models, and enhance AI strategy across teams. Responsibilities include collaboration, ownership of projects, and maximizing value through AI and data science techniques.

Lead a squad to design, develop, and operate the firmwide data catalogue (Fusion), driving secure, scalable solutions, AI-assisted engineering practices, automation, governance, and stakeholder collaboration to enable global data access and platform stability.

Ingest, map and transform customers' trade and supply-chain data into company data models. Write SQL to investigate and validate data, apply sanity checks, build simple ingestion/transformation steps, contribute to dashboards and tooling, monitor pipelines, triage alerts, and develop domain knowledge in trade and carbon accounting.

Build and modernize a scalable data platform using Databricks, Snowflake and cloud infrastructure. Develop reusable tooling, data models, orchestration, data quality checks, and automation; collaborate with cross-functional teams and prototype agentic AI workflows to improve operational efficiency for financial data pipelines.

Lead and contribute to building scalable, production-grade data systems for private markets. Architect end-to-end data workflows supporting analytics and AI/ML, ensure observability, testing, and operational maturity, influence technical strategy, engage senior stakeholders, and manage and mentor software engineers while partnering with product, data science, and business teams.

Design, build, optimize and maintain production-grade data pipelines, data models and reporting on GCP/BigQuery. Ensure data quality, governance and reliability, produce documentation, and collaborate with finance, actuary, data science, and platform teams to support analytics and MLOps capabilities.

Develop, implement, and deploy statistical and machine learning sports models and pricing engines in Python; engineer sports data assets; build automated tests; collaborate with Trading, Product, Engineering, and QA; validate data flows and integrations; and mentor junior data scientists.

As a Data Engineer, you'll develop data pipelines, manage data integration and transformation, and support analytics and intelligence projects for government clients.

Design, build, and maintain scalable data pipelines and transformations using Python/PySpark on Azure (ADF, ADLS Gen2, Synapse). Own pipelines in production, perform monitoring, troubleshooting and performance tuning, create SQL views/tables, support analytics and AI use cases, enforce data governance, collaborate with stakeholders, and maintain documentation and runbooks.

The Data Engineer will analyze, clean, and manage various datasets to assist traders at Jane Street, automating ETL processes and ensuring data quality.

Design, build, and maintain performant PostgreSQL datastores, scalable data pipelines, and production-ready Python HTTP APIs. Implement automated testing, ensure data quality, and work with researchers and clients to deliver data features for financial analysis.

Support building and maintaining data pipelines, onboard and transform partner data, monitor reliability, validate data quality, automate processes, and document pipeline logic to ensure trustworthy datasets for the business.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

Responsible for designing and maintaining data pipelines, collaborating with teams for data quality, and developing engineering solutions using various tools and technologies.

The Data Engineer will design and build production-grade data pipelines and architectures for AI solutions, ensuring data quality and collaborating with cross-functional teams.

Develop and maintain data pipelines, optimize solutions using AWS services, and ensure data quality and security while collaborating with cross-functional teams.