Top Data Engineer Jobs in London

Lead the design and implementation of modern data platforms and pipelines, ensuring high performance and compliance while consulting directly with clients to drive effective data solutions.

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

Build and maintain reliable ingestion and transformation pipelines on Databricks or Snowflake, design Data Vault 2.0/Kimball models, ensure production reliability, support testing/monitoring/documentation, and help apply LLMs and AI-enabled solutions in regulated financial services environments.

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.

Build and optimise scalable, serverless data pipelines and storage on GCP. Implement ETL, CI/CD and DataOps practices, support model productionisation with data scientists, document architectures, and guide migration toward event-driven managed cloud services.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

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.

Design, implement, and maintain data lakehouse architectures on AWS; build secure data pipelines with PII protections and compliance; advise clients across technical and executive stakeholders; manage data residency requirements; collaborate with analytics, security, and legal teams; support ML feature stores and model deployment.

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.

Design and own data architecture and ingestion pipelines that convert wet-lab instrument outputs into versioned, reproducible datasets for ML and RL training. Build tooling for dataset inspection, lineage, and integration with model training workflows, and collaborate closely with ML researchers to support pretraining, midtraining, and RL experiments.

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 Data & Insight Engineer builds AI-driven analytics environments, automating insights, managing data pipelines, and designing semantic data models for reliable business analytics.

Build and own end-to-end data pipelines and a next-generation data platform. Ingest, transform, and deliver high-quality data products for analytics and customer-facing features. Operate databases, clusters, streaming infrastructure, and improve CI/CD, automation, and developer productivity.

Build and maintain scalable GCP data pipelines and databases to support real-time AI/ML and analytics. Design data models, automate workflows with orchestration tools, ensure data security and governance, provide second-line support for incidents, optimise cost and performance, and collaborate with cross-functional teams to deliver reliable data solutions.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

Client-facing Principal Data Engineer leading design and delivery of enterprise Databricks-based data platforms. Provide architecture leadership, mentor engineering teams, drive platform optimisation, define standards, support technical governance, and deliver across multiple consultancy engagements.

Build and maintain end-to-end batch and streaming ingestion pipelines, enforce data contracts and quality, implement enrichment and lifecycle management, instrument observability and SLOs, ensure governance and enable self-service ingestion for analytics, ML, and product teams.

Design, build, and optimise cloud-based data pipelines, ETL/ELT processes and enterprise data platforms for an insurance client. Integrate multiple source systems, ensure data quality and performance, support data modelling and transformation, and collaborate with stakeholders using Agile practices.

Design, build, and maintain high-volume distributed data processing jobs and pipelines (Redshift, Athena, Iceberg, Spark, Airflow). Collaborate with data scientists and analysts to productionize models, optimize performance, troubleshoot systems, and mentor junior engineers. Support lakehouse/data warehouse management and ensure data accuracy and efficiency.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

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.

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

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 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.