Top Data Engineer Jobs in London

Leads software engineering for a secure, scalable cloud data platform supporting credit risk technology. Responsibilities include designing and delivering Python solutions, reviewing production code, building CI/CD pipelines, improving application resiliency, standardizing SDLC tooling, automating remediation, and guiding responsible AI-assisted engineering practices. The role also evaluates vendors and leads cross-functional delivery initiatives involving AWS, Databricks, Spark, Terraform, lakehouse architecture, observability, security, and governance.

Lead infrastructure engineering for critical market data platforms: provide production support, implement changes, lead architecture and automation workstreams, validate AI-assisted recommendations, ensure performance, resilience and data quality, and collaborate across platforms to modernize and stabilize trading market data infrastructure.

Build and own production data pipelines for YouTube, AVOD/FAST, Google Ad Manager, and Google Ads sources on GCP and BigQuery. Develop conformed dimensional data models, campaign-to-brand attribution, data-quality checks, reconciliation logic, monitoring, and incident response. Optimize BigQuery performance and cost while applying engineering standards, testing, CI/CD, documentation, and lineage. Partner with finance, network, and commercial stakeholders to create reliable reporting models.

Build and optimize scalable Databricks data pipelines using Delta Lake, Unity Catalog, Spark, PySpark, Python, and SQL. Collaborate in an Agile team with product and marketing stakeholders to deliver data-driven solutions. Extend the Azure data platform through Azure Data Factory and Function Apps, implement CI/CD practices, improve platform performance, and contribute to AI-assisted development and continuous modernization.

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.

Build and maintain scalable, secure data infrastructure and ETL pipelines supporting data-driven sports pricing and risk systems. Responsibilities include data architecture, relational database design, SQL and Python development, Kafka-based pipelines, AWS services, storage and recovery, data security, systems integration, troubleshooting, optimization, documentation, and collaboration with clients and cross-functional stakeholders.

Build and operate secure batch and streaming data pipelines across cloud, private-cloud, on-premise, and datacentre environments. Develop governed datasets for machine learning and quantum workloads, ensuring data quality, lineage, access control, retention, and traceability. Create reliable evaluation and synthetic-data workflows, optimize pipeline performance and costs, and implement automated testing and CI/CD while collaborating with software, platform, security, ML, and infrastructure teams.

Build and maintain batch and streaming data pipelines for the QiCore warehouse. Migrate acquired-business data into a unified platform and single customer view, model data for analytics, and ensure pipeline reliability, governance, testing, and quality. Monitor and resolve data issues at their root, support future acquisitions and migrations, and collaborate through code reviews in a fast-paced team environment.

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.

Designs and delivers scalable Azure-based data pipelines and integration solutions using Databricks, Data Factory, SQL Database, Synapse, and Data Lake. Responsibilities include ETL/ELT development, data modelling, performance optimisation, data quality, governance, documentation, and stakeholder collaboration. The role supports retail data transformation programmes and requires leadership of technical delivery or project teams, along with deep expertise in retail data domains and enterprise-scale data engineering.

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.

Build and maintain scalable data infrastructure for autonomous-driving applications. Responsibilities include developing vehicle-fleet data ingestion pipelines, distributed cloud systems, data processing and transformation services, workflow orchestration, and reliable data platforms. Collaborate with computer vision and machine learning engineers to support large-scale ML development, validation, and simulation. The role requires strong Python or object-oriented programming, distributed application development, cloud experience, RPC protocols, testing, observability, and data pipeline expertise.

Build and maintain a near-real-time financial forecast dashboard using Flourish and Canva. Integrate Binance and Bloomberg APIs, automate financial modeling and data updates, translate complex metrics into visual narratives, and optimize dashboard accuracy and responsiveness. Collaborate with advertising, design, marketing, and analytics teams to ensure brand alignment and client-focused user experience.

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.

As a Data Platform Engineer, you will design, build, and maintain Azure and Snowflake data platforms, develop Infrastructure-as-Code, and support CI/CD processes while troubleshooting and improving the platform.

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.

Lead strategic customer engagements to modernize data, analytics, and AI platforms using Microsoft Data & AI technologies. Design solutions, run workshops and proofs-of-concept, guide migrations, ensure enterprise non-functional requirements, and mentor peers while influencing technical and business stakeholders to drive adoption and measurable outcomes.

Build and own Refractal’s data foundation, including a cryptographically signed evidence store, streaming and batch pipelines, data quality and retention controls, compliance reporting datasets, and training and evaluation data. Partner with integrations, data science, and security teams to support regulated customers, ensuring strong provenance, auditability, multi-tenant isolation, data residency, and deletion controls.

Build and improve site reliability practices across the firm by implementing observability, monitoring, alerting, automation, infrastructure, and CI/CD solutions. Partner with engineering teams, support incident response and on-call operations, conduct postmortems, plan disaster recovery, instrument services, and reduce manual operational work through scalable SRE systems.

Build and own Zoopla’s production data platform, including standardized ingestion and export patterns, pipeline migration, governance, data quality, access controls, lineage, and cost management. The role involves Databricks and AWS development, infrastructure as code, AI-enabled engineering, code review, mentoring, and production support. It requires simplifying complex pipeline ecosystems while delivering business impact and collaborating with platform, analytics, and data science teams.

Design mechanical or electrical systems for high-density AI data centres, from Basis of Design through commissioning and handover. Responsibilities include developing layouts, schematics, calculations, schedules, construction drawings, modular reference designs, and engineering standards; adapting designs to codes and site conditions; managing design changes; and supporting construction, commissioning, and handover across UK and international projects.