Top Data Engineer Jobs in London

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.

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.

The role involves designing and maintaining a data platform, enhancing system efficiency, implementing data query solutions, and technical reviews within a data engineering team.

Design, build, and operate highly scalable real-time streaming microservices and data pipelines (Java/Python/Go) using Kafka, Flink/Spark on Kubernetes across multi-cloud and on-prem. Own design, deployment, monitoring, observability, and on-call support while improving platform reliability and developer experience.

Design and build cloud-based data pipelines using AWS tools, collaborate with clients, ensure data governance, and mentor junior engineers.

The Lead Data Engineer will design and oversee data pipelines and platforms, ensuring reliability, scalability, and governance while collaborating with various stakeholders.

The role involves designing and developing high-performance, real-time data processing systems using Apache Flink and Kafka, ensuring reliability and scalability while collaborating with various teams.

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

Join CrowdStrike's data engineering team to build a hyper-scale data lake for cybersecurity, using Java and Spark to develop data platforms and pipelines.

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.

Build and maintain the data foundation (pipelines, warehouse) and develop AI-native agents and internal workflows on top of it. Enable product and cross-functional teams to make faster, data-driven decisions. Measure success by improving organizational speed and intelligence.

This role involves building and maintaining a global data warehouse and automated reports while supporting campaign reporting and improving existing data solutions.

Develop and enhance low latency market data systems using high-performance C++ in a collaborative team environment, contributing to architecture and project leadership.

Design and implement a shared GCP-native data pipeline architecture for HR and Finance, build Workday and Oracle connectors, maintain a unified Cloud SQL data model and lineage graph, enable real-time event capture via Firebase, and ensure pipeline observability and Power BI DirectQuery support.

The Senior Software Engineer will scale the Supplier Data Manager for enterprise customers, enhance AI-driven data pipelines, and improve collaboration features while ensuring operational excellence and customer-driven product development.

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.

Build and maintain ETL/data pipelines into the Data Lakehouse, develop Power BI dashboards, support AWS infrastructure (S3, Glue, Lambda, Step Functions, LakeFormation), ensure data quality using dbt, write complex SQL and Python for data processing, use Git for version control, document workflows, and collaborate with analysts and data scientists.

Build and operate distributed ingestion and processing pipelines, ensure reliability and performance, define data contracts, add observability and testing, improve platform reliability and CI/CD, and participate in design/code reviews and incident retrospectives.

Design, build, and maintain scalable data pipelines and storage for fraud detection. Support fraud analytics, feature engineering, model integration and serving, pipeline monitoring, and documentation. Collaborate with data, product, and engineering teams to enable detection, reporting, and operationalisation of fraud models.

Build, deploy and operate Azure and on-prem platforms and scalable data pipelines. Maintain applications, design integrations and event-driven architectures, implement CI/CD and IaC, embed security/governance, and lead troubleshooting and continuous improvement across platform and data engineering.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

As a Junior Data Engineer, you will build data processing pipelines, support decision-making, visualize finance indicators, and monitor improvements.

Design, build, and maintain production data pipelines and warehouse/lakehouse architecture. Implement version-controlled transformations (dbt), orchestration, IaC, monitoring, and data quality. Enable secure, auditable data access for analysts and product teams, and define long-term platform strategy while partnering across engineering, analytics, and product.