Top Data Engineer Jobs in London

Develop and operate quantitative models and production systems for FX pricing, risk management, forecasting, hedging, and trading. Build Python-based quant libraries, services, data pipelines, and monitoring systems; conduct validation and backtesting; manage deployments and incident response; and collaborate with quants, traders, analysts, product managers, engineers, and risk teams.

Leads the design, development, and operation of scalable cloud and big data platforms supporting markets risk analytics. Builds batch and streaming pipelines across Databricks and Amazon Redshift, optimizes data models and workloads, establishes governance and consistency controls, and ensures production reliability through monitoring, incident management, and SLOs. Mentors engineers, drives engineering standards, and partners with risk, trading, finance, product, and other stakeholders to deliver technical solutions.

Provides principal-level technical leadership for enterprise data engineering across clinical and research platforms. Designs secure, scalable, resilient data platforms and pipelines; establishes engineering standards, architecture, governance, interoperability, metadata, and provenance practices. Leads hands-on delivery across cloud and hybrid environments while embedding privacy, security, CI/CD, infrastructure as code, automation, and observability. Collaborates across engineering, architecture, bioinformatics, product, and governance teams, mentors engineers, and shapes long-term data engineering strategy.

Own and build GetGround’s data platform and intelligence-layer foundation. Design ingestion pipelines for structured datasets, APIs, documents, and regulatory guidance; establish data quality, provenance, versioning, and reliability standards; and support AI agents and international expansion. Partner with engineering, product, and property experts to create useful data products, make architectural decisions, monitor and recover pipelines, and shape future data-team capabilities.

Leads data science initiatives from conception through deployment, developing statistical models and machine learning algorithms for trading intelligence. Collaborates with cross-functional teams to productionize solutions, supports data-driven trading strategies, and contributes to trading automation design, development, maintenance, and testing. Communicates insights to stakeholders, mentors junior data scientists, and helps solve complex business problems using large datasets.

Design, build, operate, and evolve scalable, reliable, observable data lake and catalog platform services. Lead technical design, develop well-tested software, support SLOs and incident learning, collaborate with stakeholders, create implementation plans, mentor engineers, and contribute to platform architecture and open-source initiatives.

Build and scale backend and data systems supporting Spotify’s content intelligence platform. Design distributed infrastructure for high-throughput processing of audio, video, text, and images; own projects from architecture through operation; contribute to RFCs, AI/LLM workflows, production integrations, engineering standards, and on-call support. Collaborate with engineering and technical stakeholders to deliver reliable, scalable, performant, and cost-efficient solutions.

Lead data architecture and engineering for Wise’s marketing platform, building resilient high-volume streaming and batch pipelines. Develop Airflow and dbt workflows for unified customer datasets, establish data quality and reliability standards, and bring MarTech integrations in-house. Scope ambiguous technical problems, define roadmaps, collaborate with engineering, analytics, product, and marketing stakeholders, and mentor software engineers on data best practices.

Leads software engineering for an AI/ML data platform, designing, developing, testing, troubleshooting, and operating secure, scalable Python applications in containerized cloud-native environments. Partners with product and data teams, promotes responsible AI-assisted development, automates recurring remediation, evaluates technology solutions, and mentors junior engineers.

Develop and support secure, scalable software for AI/ML data platforms. Responsibilities include Python architecture and development, cloud-native and containerized application delivery, code review, testing, troubleshooting, operational stability, and automation of recurring remediation. The role also promotes validated AI-assisted engineering practices and collaborates with product managers and data strategy teams. Preferred experience includes graph databases such as AWS Neptune and generative AI engineering pipelines.

Designs and maintains scalable data architectures, ETL pipelines, storage, backup, recovery, and secure data systems. Writes and optimizes SQL and Python, manages relational databases and streaming pipelines, and supports AWS-based data infrastructure. The role also involves systems integration, data security, troubleshooting, documentation, and collaborating with clients and stakeholders to deliver data engineering projects.

Build and maintain batch and streaming data pipelines for a modern AWS sportsbook data platform. Develop medallion-aligned datasets using SQL, dbt, Spark, and related tools; support Kafka or Flink streaming; implement data quality checks, schemas, monitoring, and governance; and troubleshoot pipeline performance and costs. Collaborate with analytics, data science, machine learning, trading, risk, and personalization teams to deliver reliable, scalable data assets.

Design, provision, and operate a scalable, secure cloud data platform on AWS and Databricks using Terraform, automation (Python/Java), CI/CD, observability, and security best practices to support production analytics workloads.

Design, build, and maintain reliable data pipelines, models, automation, and Snowflake environments for DK and PRH. Integrate internal and external data sources, support BI reporting with Power BI and Microsoft Fabric, manage CI/CD, data governance, monitoring, documentation, testing, and incident resolution. Collaborate with technical and business stakeholders to deliver secure, scalable, high-quality data solutions and contribute to technical reviews and best practices.

Lead the design, development, and optimisation of scalable Databricks data pipelines and models. Integrate batch and real-time data sources, improve data quality and observability, and embed security and governance standards. Provide technical leadership, solve complex engineering problems, influence platform architecture, and mentor Data Engineers while partnering with stakeholders and external teams to deliver reliable, production-grade data solutions.

Graduate data engineering role focused on building and maintaining SQL/dbt transformation pipelines, Python ingestion services, API data pipelines, and data quality processes. The role includes production coding, testing, debugging, code reviews, cloud infrastructure exposure, AI-assisted engineering, and collaboration with engineers, analysts, and product stakeholders. It is designed for recent graduates with foundational SQL and Python experience and offers mentorship, training, and cross-functional development opportunities.

Build and operate large-scale data ingestion, document processing, search, and serving systems for AI products. Responsibilities include handling massive heterogeneous datasets, designing schemas and indexes, enabling keyword, vector, and structured search, linking information across sources, and optimizing performance, reliability, latency, and cost. The role owns systems end to end, from source acquisition through user-facing functionality, and collaborates with AI researchers and product engineers.

Develop and maintain enterprise-scale cloud data pipelines using Microsoft Fabric and Azure Data Platform technologies. Support ETL/ELT processes across Medallion Architecture, data lakehouse and warehouse solutions, data quality, governance, testing, reporting, and Power BI integration. The role involves PySpark, SQL, Python, Azure DevOps, CI/CD, code reviews, Agile collaboration, and continuous learning under the guidance of experienced engineers and architects.

Build and maintain scalable data platform solutions supporting analytics and business decision-making. Collaborate with product, analytics, and engineering teams to solve complex problems, improve data reliability, manage infrastructure, and enable fast insights. Responsibilities include developing data-intensive systems, working with cloud platforms and warehouses, applying infrastructure-as-code and CI/CD practices, and balancing scalability, cost, security, and delivery speed.

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.

The Senior Data Platform Engineer will manage Databricks infrastructure, develop cloud systems, collaborate with teams, and enhance machine learning workflows.

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.

Designs and maintains scalable data pipelines and platforms using Palantir Foundry, cloud services, Python, PySpark, and TypeScript. Builds Foundry datasets, applications, integrations, and AI-enabled workflows; develops user-facing solutions with Workshop and Slate. Partners with stakeholders to deliver enterprise data products, while implementing data quality, governance, security, performance optimization, CI/CD, DevOps, and cloud-native deployment practices for insurance-sector initiatives.

Develop and maintain customer-facing analytical reporting, data pipelines, and data models. Build Python applications, ensure data quality and consistency, collaborate with analytics engineers, and enable business teams to access actionable data. The role also involves cloud data warehouse optimization, infrastructure as code, software engineering best practices, and cross-functional product collaboration within an agile SaaS environment.

Lead software engineering initiatives for data platforms within an agile team. Provide technical guidance, develop and review secure production code, solve complex design and functionality problems, and influence application architecture and technical operations. Drive responsible adoption and governance of AI-assisted engineering tools, including code review, refactoring, testing, troubleshooting, and incident analysis. Serve as a subject matter expert, promote engineering standards, and contribute to firmwide software development practices.