Top Data Engineer Jobs in London

Build, optimise and stabilise AWS-based data pipelines (S3, Glue, Redshift, Lake Formation). Resolve technical debt, support the Architect on target-state design, collaborate with client engineering teams to transfer knowledge, and deliver production-ready platforms in regulated, high-volume gaming environments.

Support building and maintaining a modern data lakehouse platform: implement storage standards, develop ingestion and transformation pipelines, capture lineage, enforce data quality, collaborate with analytics and business teams, follow governance/security practices, participate in agile delivery, and troubleshoot issues with senior engineers.

Design, build, operate and optimise scalable data pipelines and platform components. Deliver production-ready solutions, support production incidents, improve data quality, and collaborate across engineering and analytics teams.

Drive business development for Victaulic within the UK data centre market by identifying major projects, engaging owners and engineers, expanding account penetration, coordinating with regional and international sales teams, documenting opportunities in Salesforce, and planning activities to meet sales targets. Promote products through stakeholder engagement, presentations, and industry association participation. Frequent regional travel and overnight stays required.

Design and deliver scalable Customer Data Platform solutions (Oracle Unity), build and operate data pipelines and Customer 360 models, integrate CDP with data lake/CRM/marketing systems, lead architecture and data modelling workshops, ensure data quality, governance, security, and support CDP operations, and partner with Oracle implementation teams.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Design, build, and maintain large-scale distributed data systems and pipelines for blockchain analytics. Ship production code, drive technical direction, mentor engineers, improve reliability, observability, and cost efficiency, and collaborate with product, data science, and intelligence teams.

As a Senior Data Engineer, you will design and manage data ingestion pipelines, ensuring scalability, reliability, and quality of data for analytics and machine learning purposes. You'll collaborate with cross-functional teams to define data standards and governance while promoting self-service and standardization across the organization.

Lead technical strategy for enterprise analytical data modelling, analytics automation and engineering empowerment. Build reusable modelling patterns, automation (including Snowflake Cortex and LLMs), self-service frameworks, mentor engineers, and drive platform modernisation to enable squads to deliver scalable, trusted Data Products.

Design and build a greenfield marketing data foundation: tracking/tagging, identity strategy, data pipelines, integrations (GA4, Salesforce, Databricks, COGNOS), data modeling and QA, dashboards, attribution/MMM, governance and compliance. Serve as primary contact for marketing data issues, enforce standards, prioritize analytics backlog, and enable stakeholders.

The Data Engineer will implement cloud-native data platforms, engineer scalable pipelines, and optimize cloud resources using Python and SQL, while applying agile methodologies and cloud automation tools.

Lead design and evolution of a company-wide data platform spanning streaming, batch, warehousing, orchestration, governance, and ML enablement. Deliver core platform capabilities, reliable real-time pipelines, shared tooling, and scalable infrastructure. Drive cross-team initiatives, champion golden paths, improve reliability/cost/observability, ensure regulatory compliance, and mentor senior engineers while influencing technical strategy and standards.

Design, build and maintain cloud-native data platform capabilities across Azure, Databricks and Microsoft Fabric. Deliver reusable, secure, and automated platform components (IaC, CI/CD), embed security-by-design, enable federated data use for analytics, ML and reporting, and support observability, monitoring, and platform reliability while collaborating with product, engineering, security and business stakeholders.

Lead and optimise the data platform architecture, coach and grow the data team, build and maintain production data pipelines and sources (APIs, SQL/NoSQL, parquet), implement ML infrastructure and MLOps, deliver data engineering projects with stakeholders, and collaborate with engineering teams to integrate back-end databases.

Design and implement end-of-day market data sourcing, validation, modeling, and delivery for pricing and risk. Build Snowflake data models and ETL/ELT pipelines, integrate AWS SQS for data readiness, optimize performance, ensure data quality and auditability, and collaborate with Market Data and Risk teams.

Lead design and operation of a scalable Databricks-based data platform: build pipelines, define medallion modelling, set engineering and governance standards (Unity Catalog/PII), enable self-service, mentor engineers, and own architecture and IaC best practices to drive a data-driven fintech product.

The Senior Data Platform Engineer will lead the development of scalable Snowflake services, automation, CI/CD pipeline design, and security controls to optimize data-driven healthcare projects.

As a Data Software Engineer, you will enhance reference data products, establish technical direction, and ensure data quality and compliance within trading systems.

Build and evolve a reusable data and ML platform: implement IaC and CI/CD, develop Python/Spark components, support data/ML lifecycle and AgentOps, improve observability, enforce security and governance, and collaborate across engineering and data teams.

Design, build, and maintain scalable data pipelines and platform components using Spark/PySpark on Azure. Develop reusable templates, enforce data engineering best practices (CI/CD, testing, observability), support product teams, and optimise pipeline performance, reliability, security, and cost-efficiency.

Design, build, and maintain cloud-native backend services for index data processing. Write production-ready Python, deploy and operate services on AWS, use Infrastructure as Code, and monitor/debug systems with observability tools like Prometheus and Loki. Collaborate with product, research, and engineering teams.

Senior technical lead responsible for designing, owning and delivering a secure multi-region cloud data platform (OCI/Databricks). Leads platform architecture, sets engineering standards, manages delivery and mentors data engineers while supporting vendor partnerships and regulated data compliance.

As a Senior Data Engineer, you will design, maintain data pipelines and production ML systems, ensuring data reliability and performance. You'll collaborate with teams, support junior members, and work on cloud infrastructure, mostly in AWS.

The Senior Data Engineer will develop, maintain, and support data pipelines and artefacts for SCOR's data ecosystem, ensuring high-quality data management and promoting best practices across teams.

Design and implement cutting-edge data solutions for AI-driven transformations, collaborating with clients, developing scalable architectures, and supporting AI integration.