Top Data Engineer Jobs in London

Design, build, and operate cloud-native data platforms and pipelines (batch and streaming) to support analytics and regulatory reporting. Implement data models, orchestration (Airflow), infrastructure-as-code (Terraform), containerized deployments, and maintain testable Python/PySpark code and data-quality frameworks.

Design, build, test, and maintain scalable ELT data pipelines and architectures using Python and Databricks. Ensure data security, entitlement controls, and SDLC quality (CI/CD, testing). Use SQL/NoSQL, update data models, apply enterprise-authorized AI responsibly, and support controls and auditability across the data lifecycle.

Design, build, and operate enterprise-grade, cloud-native data platforms and pipelines for Preqin's Company Intelligence. Provide technical architecture, end-to-end delivery, data governance, observability, and reliability while leading and mentoring a small team and partnering with product and analytics to deliver measurable business value.

As a Senior Azure Data Engineer, you'll design and deploy data engineering pipelines using Databricks on Azure, collaborate with clients, and implement best practices in a consulting environment.

Lead architecture and delivery of data solutions on Databricks within Azure, ensuring best practices in data pipeline design and implementation.

The role involves designing and building data pipelines using AWS technologies, leading projects, and mentoring junior engineers in a data engineering context for financial services.

Develop, implement, and deploy statistical and machine learning sports models and pricing engines in Python; engineer sports data assets; build automated tests; collaborate with Trading, Product, Engineering, and QA; validate data flows and integrations; and mentor junior data scientists.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

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 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.

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 real-time data pipelines and AI-enabled data engineering for analytics and trading. Guide technical direction, mentor engineers, drive SDLC improvements, and collaborate with research and trading teams to onboard datasets and build production-grade tools and frameworks.

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, 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 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 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.

Lead the design, build, and operation of scalable data platforms and RAG-enabled GenAI pipelines. Implement cloud GenAI services, batch and streaming data workflows, testing strategies, and enterprise security/compliance. Mentor engineers, drive adoption of AI-assisted development practices, and collaborate across data, platform, and product teams to deliver reliable, production-ready data and AI services.

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.

Design, build, maintain, test and optimize scalable data architectures and pipelines. Extract, transform and load data for analysts and scientists, ensure data quality, collaborate with architecture teams, adopt DevOps practices, and source/integrate new data to support bank products and cost-effective platform solutions.

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 real-time and batch data pipelines, consolidate data into data lakes/warehouses, implement ETL/ELT and data modeling best practices, develop transformations and notebooks, create interactive reports, and collaborate with engineers on data quality, testing, validation, and documentation.