Top Data Engineer Jobs in London

Design, build, test and maintain ETL/ELT pipelines and analytics warehouse (Snowflake/dbt). Implement data testing, governance, observability and self-service capabilities. Partner with analysts, data scientists and stakeholders, troubleshoot data issues, and mentor colleagues to improve data reliability and analytics processes.

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.

As a Data Engineer, you'll optimize the Data Lake and develop pipelines for data-driven decision-making, collaborating with Data Science for efficiency.

The Data Engineer will design and architect data systems for real-time workloads, mentor junior engineers, and ensure data reliability and security.

As a Senior Backend Engineer, you will architect and build scalable data systems using Kotlin, design distributed analytics infrastructure on AWS, and deliver analytics features for insurers, improving their risk decision-making.

The Data Engineer Manager will create an AI-ready data factory on Azure and Snowflake, ensuring high-performance data products through best-in-class engineering practices and team leadership across multiple regions.

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

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

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.

As a Data Engineer, you will design and maintain ETL services, collaborate in agile teams, and utilize cloud technologies to build scalable data solutions.

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.

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

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 and maintain cloud-based data pipelines and analytical tooling for crypto market surveillance. Perform systematic and ad-hoc analysis to detect market abuse, support investigations, publish crypto disclosures, and enhance surveillance platforms using big-data, visualization, and blockchain analytics tools.

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.

As a Senior Data Scientist, you will develop and implement statistical models and machine learning algorithms to enhance sports modeling and the Sportsbook experience, collaborating across teams and mentoring junior 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.

The Data Engineer Operations will design automated tools and dashboards to optimize trading operations, manage trade lifecycle processes, ensure accurate reporting, and improve workflows through collaboration and automation.

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

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

Design, implement, and improve Spark-based data pipelines for Veeva Link's data platform. Own features end-to-end, collaborate with data science to operate ML models, and enhance observability, performance, and precision.

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.

Lead data architecture and strategy across OLTP, OLAP, and AI systems. Drive AI-first foundations (LLM orchestration, RAG, real-time inference), ensure DataOps/CI-CD, mentor technical leads, run architectural reviews, partner with product and engineering leadership, and optimise platform security, observability and costs.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.