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, 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.

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.

Lead software engineering initiatives for data platforms within an agile team. Provide technical guidance, develop and review secure production code, solve complex design and functionality problems, and influence application architecture and technical operations. Drive responsible adoption and governance of AI-assisted engineering tools, including code review, refactoring, testing, troubleshooting, and incident analysis. Serve as a subject matter expert, promote engineering standards, and contribute to firmwide software development practices.

Own and operate production Python infrastructure for FX pricing, risk, and trading systems. Responsibilities include developing microservices, shared quantitative libraries, monitoring and alerting, CI/CD, deployment, incident response, and root-cause analysis. The role also offers growth into curve construction, pricing models, trading strategies, risk modeling, backtesting, and customer behavior modeling while collaborating with quants, traders, analysts, product managers, and engineers.

Design, implement, operate, and troubleshoot CrowdStrike’s global multi-vendor, multi-cloud, hybrid data center networks. Build cloud connectivity and network constructs, develop infrastructure-as-code and automation using Ansible, Terraform, Python, and Go, improve resilience and deployment efficiency, support incident analysis and on-call rotations, and ensure compliance with regional data sovereignty and security requirements.

Build and maintain Azure-based data pipelines, warehouses, Data Vault and dimensional data marts using ADF, Azure SQL, and T-SQL. Develop Power BI datasets, semantic models, and reports; optimize stored procedures; investigate source systems; and implement data quality monitoring. The role also involves profiling datasets, validating business results, debugging data platforms, and collaborating with clients and a remote European team.

Work in multidisciplinary teams to design, build and maintain data pipelines, models and analytics products using SQL, Python and modern data tools. Contribute to cloud-based data solutions (AWS/Azure), apply software engineering and data governance practices, create visualisations, and collaborate with stakeholders in Agile delivery to produce maintainable, production-ready data services.

Build and maintain reliable financial datasets for quantitative researchers and traders. Responsibilities include extracting, cleaning, aggregating, and onboarding data from diverse sources; developing robust extraction processes; investigating data quality issues; and improving onboarding tools. The role requires close collaboration with researchers, traders, engineers, management, and external data providers in a fast-paced quantitative investment environment.

Designs and delivers scalable data solutions on Azure, collaborates with engineers and architects, supports data and telemetry understanding, performs proof-of-concept work, and builds reliable, tested, maintainable systems using CI/CD. The role contributes to agile delivery, engineering standards, data modelling, performance and cost optimization, while developing expertise in modern data technologies.

The Data Engineer will build and maintain data pipelines, ensuring quality and performance while collaborating with teams to support platform evolution and product delivery.

Designs, builds, and maintains scalable AWS data platforms, ETL/ELT pipelines, data models, and processing workflows. Uses Python, SQL, infrastructure-as-code, CI/CD, monitoring, and data quality practices to deliver secure and resilient solutions. Collaborates with architects, security specialists, and clients; troubleshoots complex issues; establishes engineering standards; and mentors other engineers. The role also requires technical ownership and consideration of governance, access control, and regulated-environment requirements.

Build and operate Python-based tools, data pipelines, analytical models, dashboards, and AI/ML solutions for Finance. Partner with Finance users to automate reporting, reconciliations, controls, and workflows. Deploy solutions on Azure using secure, governed software engineering practices, CI/CD, monitoring, and documentation. Collaborate with Data Engineering, Technology, and Risk teams while coaching Finance colleagues on Python, automation, and data tooling.

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.

Leads the design, development, and support of enterprise data platforms and pipelines. Ingests, cleanses, models, and documents data from diverse sources into governed Kimball datasets for BI, machine learning, and AI. Oversees data engineers, ensures pipeline reliability and quality, applies DevOps and CI/CD practices, performs data analysis, and collaborates with stakeholders on requirements, governance, GDPR compliance, and continuous process improvement.

Build and optimize Lakehouse data pipelines (Trino + Iceberg) using Medallion Architecture to transform high-throughput event logs into audit-ready subledgers and reporting metrics. Implement stream processing with Kafka, embed automated reconciliation/data quality checks, collaborate with finance and product stakeholders, and mentor junior engineers.

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.

Build secure, scalable AI/ML platforms and applications to productionize research. Collaborate with AI researchers and data scientists, implement CI/CD and IaC, support experimentation environments, automate remediation, mentor junior engineers, and promote secure, responsible AI development practices.

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.

Maintain and update regulatory models in SAS Viya production, deliver critical month-end reporting data, and support data quality processes. Liaise with Credit Risk stakeholders, implement governed production-code changes, manage incidents through ServiceNow, and ensure compliance with audit and regulatory requirements. The role also contributes to data governance, automation, continuous improvement, cloud warehouse initiatives, and future Virgin Money integration projects.

Build, optimize, and maintain secure batch and streaming data pipelines using Snowflake and cloud platforms. Develop data models, support ingestion and transformation, apply governance and security practices, and collaborate with architects, engineers, analysts, and clients. The role also involves performance tuning, data solution delivery, technical leadership, mentoring engineers, and contributing reusable assets and best practices to the data engineering practice.

Designs and delivers scalable batch and streaming data pipelines using Databricks and Lakehouse architectures. Responsibilities include ETL/ELT development, data modeling, Delta Lake optimization, governance, security, orchestration, and cloud integration. The role is client-facing and involves collaborating with architects, engineers, ML teams, analysts, and senior stakeholders. It also requires mentoring engineers, improving data practices, enabling analytics and AI use cases, and contributing reusable assets and technical leadership.

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.