Top Data Engineer Jobs in London

As a Data Engineer, you will design and build data pipelines, models, and infrastructure for analytics and machine learning while ensuring data quality and compliance.

The Data Engineer role involves modeling data as an Analytics Engineer, improving data infrastructure, managing data ingestion, and delivering training.

As a Data Engineer, you'll develop and maintain data pipelines, integrate complex datasets, and ensure data quality for quantitative trading strategies.

The Principal Data Engineer will design and build AI features, manage data pipelines, and collaborate with data scientists to enhance machine learning models and their implementations.

Lead data architecture and resiliency for marketing data pipelines. Build scalable Airflow/dbt workflows, productionize CDP datasets, own MarTech integrations, bridge engineering and analytics, and mentor engineers to improve reliability and observability for high-volume streaming and batch systems.

Lead design and productionization of scalable streaming and batch data pipelines (Python, Kafka, Trino, Iceberg). Build unified customer datasets using Airflow and dbt, own MarTech integrations, set data architecture and reliability standards, partner with analytics and marketing, and mentor engineers.

Lead engineering of metadata, meta-modelling, and distributed systems; produce and review secure production Scala/Java code; drive adoption and governance of AI-assisted engineering across the SDLC; influence product design and operations; mentor technical teams; establish automation, validation standards, and reuse of proven patterns.

Lead the design and delivery of metadata, meta-modelling, and distributed systems. Develop and review secure, high-quality Scala/Java production code, adopt AI-assisted engineering practices, improve automation and operational stability, and apply cloud-native and SDLC best practices within an agile team supporting financial services applications.

Lead Data Engineer responsible for designing, building, and maintaining secure, scalable data collection, storage, access, and analytics solutions. Ensure data trustworthiness, create data models, implement backup/recovery/archiving, evaluate access control, and support data platform stability across business functions.

Lead design and delivery of metadata, meta-modelling, and distributed systems. Build secure, high-quality production code, drive adoption of AI-assisted engineering, improve SDLC automation and cloud-native delivery, set validation and governance standards, mentor engineers, and influence product and technical decisions.

Develop, test, and support reliable, high-performance distributed Java applications for real-time streaming and concurrent workloads. Partner with stakeholders, present and review designs, improve reliability/observability/performance, and support production releases to meet SLAs within BGM Data & Analytics.

Design, build, and maintain scalable cloud-native data pipelines and lake/lakehouse infrastructure for multi-modal scientific data. Optimize storage layouts and access patterns for analytics and GPU-based ML training, implement versioning, lineage, and quality monitoring, operate workflow orchestration and containerised environments, and collaborate closely with data scientists and ML researchers to enable efficient model training and research workflows while meeting security and governance needs.

As a Quant Data Engineer, you'll support quant modellers by preparing and maintaining datasets, improving data quality, and building Python-based data workflows for analysis and modelling.

Design and deliver a real-time liquidity observability stack and core treasury functions. Build scalable, event-driven systems, implement monitoring and control mechanisms, automate cash investment and risk tooling, and collaborate with product, data scientists and stakeholders to forecast cash flows and optimize liquidity.

Design, build, and operate governed data pipelines and context layers for AI agents integrating Salesforce, SAP, and service data. Implement feedback loops, data-quality frameworks, semantic models, Power BI reporting, and secure API-based integrations to support enterprise commercial AI workflows.

Serve as the technical authority for enterprise customers in data security, owning the technical sales process. Deliver technical presentations, demos, and whiteboard sessions, advise on AI/ML-driven data protection, and drive outcomes through value-based selling with partners and GTM teams.

Build, maintain and improve scalable ETL/ELT pipelines, data warehouses and streaming systems. Ensure data modelling meets analytics and data science needs, uphold data engineering best practices (testing, CI/CD, observability, IaC), and collaborate across teams to deliver the technical roadmap and platform improvements.

Build and maintain data pipelines and ETL processes using Python and SQL; transform and model data with dbt and Snowflake; automate workflows, create tools to optimise operations, and collaborate with technical and non-technical stakeholders.

Design, develop, and maintain data pipelines using Kafka; build infrastructure with Terraform; troubleshoot data engineering issues and collaborate with teams.

Design, build, and maintain performant PostgreSQL datastores, scalable data pipelines, and production-ready Python HTTP APIs. Ensure data quality, integrity, and query performance for financial datasets. Collaborate with researchers and clients to translate needs into features, using automated testing to own features from conception through deployment.

Build and maintain production-grade data pipelines, design data models and infrastructure, enable API access, govern data assets, and collaborate with actuaries, data scientists, and engineers to support algorithmic underwriting and MLOps.

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

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.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure to support data-driven decision-making in a collaborative environment.