Top Data Engineer Jobs in London

Design, build, and maintain scalable ETL/data pipelines and storage to support analytics and ML across international markets. Ensure data quality, compliance (e.g., GDPR), monitoring, and automation. Collaborate with data scientists, product teams, and stakeholders to deliver reliable datasets and contribute to the data infrastructure roadmap. Onsite in London.

Design, develop and automate data pipelines ingesting transactional, social and geographic data. Build crawlers and APIs, develop algorithms and statistical models (including clustering) for large-scale graph data, and improve infrastructure and internal tools.

Design, build, and operate a high-throughput, low-latency real-time data platform using Flink and Kafka. Develop Java and .NET processing and APIs, manage cloud infrastructure (AWS, Terraform), implement CI/CD (Harness), and ensure observability, scalability, performance, and cost-efficiency across production systems.

Lead data migration strategies and implementation across hybrid cloud environments, design data pipelines, and optimize scalable systems while collaborating on AI projects.

Join the Data Strategy team to design, build, and maintain cloud-based ETL/ELT pipelines and data services using Python, SQL, Spark, and Databricks. Integrate heterogeneous data sources, prepare curated datasets, support production operations, and contribute to migration and platform modernization efforts for analytics and downstream consumers.

Lead a squad to design, build, and operate the firmwide data catalogue (Fusion). Provide technical leadership, drive platform enhancements, enforce secure AI-assisted development practices, promote automation and governance, and collaborate with stakeholders to deliver scalable, reliable data platform solutions while mentoring engineers and improving operational stability.

Work in multidisciplinary teams to design, build and maintain data pipelines, models and analytics products using SQL, Python and modern data tools. Contribute to cloud-based data solutions (AWS/Azure), apply software engineering and data governance practices, create visualisations, and collaborate with stakeholders in Agile delivery to produce maintainable, production-ready data services.

Lead design and delivery of scalable GenAI platforms and services: build RAG pipelines and agents, model/version management, monitoring and drift mitigation, cloud-native microservices and deployment, testing and guardrails to reduce hallucinations, ensure compliance, and mentor engineering teams.

Lead reliability, performance, and automation of analytics pipelines and reporting. Define SLIs/SLOs, monitor data quality/lineage, enable self‑service dashboards, enforce data risk/controls, drive FinOps/capacity insights, lead stakeholder reviews, and mentor junior engineers.

Lead design and delivery of cloud-native data and AIML production systems. Build data engineering, backend, Cloud infra/DevOps and MLOps stacks, produce secure production code, architect microservices and distributed, data-intensive applications, and drive adoption/governance of AI-assisted engineering practices while communicating with stakeholders.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Lead design and deliver cloud-native, production-scale data and AI/ML engineering solutions. Build data engineering, backend, DevOps and MLOps stacks; produce secure production code and architecture artifacts; troubleshoot complex systems; collaborate with stakeholders and engineering communities to industrialize AI/ML models.

Lead design, delivery and maintenance of on-prem and cloud market-data distribution systems (real-time, historical, AI/ML use cases). Collaborate with product/stakeholders, provide 3rd-level operational support, validate AI-assisted recommendations, ensure resiliency, security, auditability and strong process controls.

Lead a technical area driving design, development, and delivery of AI/ML and data platform solutions. Own architecture, governance, and adoption of AI-assisted engineering practices, ensure operational stability, and influence stakeholders while hiring and developing engineering talent.

Design, build, and maintain large-scale, secure data engineering solutions. Lead architecture, deliver production code, drive automation and operational stability, mentor teams, and evaluate vendor/technology choices to advance risk technology platforms.

Design, build, and operate cloud-native lakehouse and streaming data platforms and pipelines using Python, PySpark, dbt, Airflow, and orchestration/infrastructure tooling. Ensure performance, reliability, security, and observability; model and transform data for analytics; deploy with IaC, containers, and Kubernetes; mentor engineers and drive platform standards and best practices.

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.

Lead client-facing data platform architecture and strategy. Design scalable Databricks/Azure or AWS-based solutions, oversee multi-team delivery, incident response, governance, and data security. Mentor engineers, drive roadmap and budget decisions, and ensure resilient, high-quality data pipelines aligned to business goals.

The Senior Simulation Data Engineer will manage the end-to-end data pipeline for simulation, ensuring high-quality outputs for ML training and collaborating with research teams to optimize processes.

Lead the design of data platforms for AML/KYC and Fraud products, shaping data systems, coaching engineers, and setting technical direction across teams.

Build and maintain automated data pipelines on the Databricks lakehouse using SQL, Python and PySpark. Produce production-ready data models, implement data quality checks and tests, follow Git/GitHub workflows, collaborate with consultants on healthcare data problems, and learn best practices from senior engineers.

Design, build, and maintain end-to-end data ingestion pipelines and data architecture. Transition datasets to a new stack, map/standardize/normalize data, monitor data quality, manage vendor data inputs, and partner with commercial trading teams to gather requirements and support analytics and investing use cases.

Entry-level data engineer to follow a structured 12-month development programme. Build internal reporting, reusable engineering frameworks, documentation, and tooling while learning SQL, Python, Databricks and cloud data services. Support senior engineers, join code reviews, shadow customer workshops, and progressively contribute to client projects.

As a Data & AI/ML Architect, you will conduct workshops, oversee project delivery, design architectures, and mentor juniors while engaging with clients.

Design, build and maintain cloud-native ETL/ELT pipelines and data lake solutions using dbt, Snowflake/Databricks and AWS services. Optimize SQL and data models for performance and cost, create dashboards, follow CI/CD and software engineering best practices, and collaborate with architects, engineers and clients to deliver production-ready enterprise data solutions.