Top Data Engineer Jobs in London

Design, develop, and support cloud-based data solutions across AWS and Azure. Build ETL/ELT pipelines, data integrations, migration processes, warehouses, lakes, and lakehouses. Perform data profiling, quality assessments, validation, reconciliation, troubleshooting, and root-cause analysis. Collaborate with technical and business stakeholders while supporting governance, security, compliance, and technical documentation.

Build and improve Power BI dashboards, data models, measures, and reports. Translate business questions into actionable insights, validate and reconcile SQL data, identify trends, improve data quality, consolidate reporting, and support governance, security, and deployment standards. Collaborate with IT, Finance, and other stakeholders while enabling users to understand and trust business intelligence solutions.

Build end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

Owns the architecture, development, and scaling of data products, backend data services, ingestion engines, APIs, and infrastructure on AWS and Snowflake. Collaborates with engineering and data teams, automates infrastructure using AWS CDK, optimizes reliability and latency, and ensures data quality through testing and validation. The role applies a product mindset to customer-facing and internal data services within an EV charging and smart energy platform.

Build and maintain Kafka Connect services and streaming data pipelines for Depop’s data platform. Extend the bronze streaming layer, implement data contracts and freshness checks, develop quarantine and alerting systems, support event onboarding and troubleshooting, document technical decisions, and collaborate with senior engineers and teams across data infrastructure, ML, and insights.

Design, build, and operate scalable data pipelines, models, cloud platforms, and AI applications on Google Cloud. Lead technical delivery for client projects, make architecture decisions, manage risks, and ensure production quality through testing, observability, and data controls. Build and evaluate LLM applications, collaborate with consultants and data scientists, communicate with technical and non-technical stakeholders, and raise engineering standards through reviews, documentation, and reusable practices.

Build and operate production batch and streaming data pipelines across ingestion, transformation, reconciliation, and client delivery. Develop CDC, incremental loading, dimensional models, medallion datasets, data contracts, quality controls, monitoring, and CI/CD workflows. Support Snowflake, Databricks, SQL Server, and Azure platforms while improving architecture, security, tenant isolation, performance, and reliability. Investigate incidents and collaborate with product, valuation, client-facing, and technical teams.

Build scalable data pipelines, data models, feature stores, and cloud-native data applications supporting machine learning workloads. Partner with Machine Learning Engineers and Data Scientists to create reliable datasets, maintain production pipeline quality and observability, and support batch and real-time data systems. The role uses Python, SQL, Spark, Airflow, AWS, CI/CD, and modern data engineering technologies.

Build scalable data pipelines, platforms, datasets, and architectures supporting machine learning, analytics, pricing, forecasting, customer insights, and marketing decisioning. Use Python, Spark, Databricks, and cloud technologies to develop reliable, observable, cost-efficient data products. Partner with data scientists and machine learning engineers, operationalize models, improve engineering practices, mentor colleagues, lead technical designs, and collaborate with commercial stakeholders.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Design, implement, operate, and troubleshoot CrowdStrike’s global multi-vendor, multi-cloud, hybrid data center networks. Build cloud connectivity and network constructs, develop infrastructure-as-code and automation using Ansible, Terraform, Python, and Go, improve resilience and deployment efficiency, support incident analysis and on-call rotations, and ensure compliance with regional data sovereignty and security requirements.

Build and operate backend data infrastructure for satellite laser ranging measurements, including ingestion pipelines, high-volume time-series storage, customer APIs, and secure data delivery. The role requires production backend engineering, cloud infrastructure, containers, observability, testing, and collaboration with front-end, geodesy, and orbit determination teams.

Lead mechanical engineering design and delivery for complex data centre projects. Design resilient cooling, ventilation, automation, controls, and ancillary systems; coordinate with electrical and multidisciplinary teams; manage technical delivery, resources, timelines, and client relationships; produce and review detailed designs; supervise engineers; and apply building physics, CFD, sustainable design, CAD, Bluebeam, and BIM tools.

Develops interactive, static, and animated data visualizations that transform complex commodity, maritime, and geopolitical datasets into actionable narratives. The role involves querying and validating data with SQL and Python, applying GIS and spatial analysis, building web-based interactive experiences with JavaScript or TypeScript, collaborating cross-functionally, and mentoring peers on visualization and storytelling standards.

Design and build a research platform for reproducible scientific analysis, including reprocessing pipelines, evaluation harnesses, cloud compute and data access, engineering standards, CI, testing, and regulatory traceability. Partner with scientists to create usable tooling, improve research-to-software workflows, mentor junior developers, and establish the platform engineering function. The role requires deep Python and experience with reproducible scientific or quantitative workloads.

Build and maintain Python data ingestion pipelines for Reach’s AWS data lake, integrating APIs, S3, and SFTP sources. Design and operate AWS infrastructure, implement Infrastructure as Code and observability, and work with Athena, Glue, Lambda, ECS, and Redshift. Collaborate with analysts, product managers, and business teams, while documenting solutions, enforcing unit testing, and improving data quality and reliability.

Leads enterprise data engineering strategy and delivery across Databricks, dbt, Azure, Python, and SQL. Defines architecture and engineering standards while driving modernization, automation, security, resilience, governance, and compliance. Influences stakeholders, coaches engineering teams, simplifies technology landscapes, and aligns data, cloud, analytics, and AI investments with business outcomes in a regulated environment.

Presents Salesforce data, integration, architecture, governance, analytics, and related solutions to public-sector and nonprofit customers. Conducts discovery sessions, leads workshops, develops customized demonstrations, explains business value, resolves technical objections, and collaborates with sales teams on differentiated solutions. Manages multiple customer engagements, builds trusted-advisor relationships, and travels occasionally.

Lead data science initiatives from conception through deployment, developing statistical models and machine learning algorithms for trading intelligence. Analyze large datasets, integrate solutions into production systems, support automated trading strategies, communicate insights to stakeholders, and mentor junior data scientists. Collaborate cross-functionally to improve trading processes, product offerings, and business operations.

Build and own high-performance distributed data systems supporting multimodal AI workloads. Responsibilities include designing, deploying, monitoring, and scaling storage, indexing, querying, processing, protocols, and APIs. The role requires production service ownership, sound architectural judgment, performance optimization, reliability engineering, and mentoring through code reviews, pairing, and knowledge sharing.

Build and operate backend services, APIs, data catalogs, lineage systems, and publishing platforms for Spotify’s data ecosystem. Develop batch and streaming pipelines, manage metadata and schemas, ensure data quality and reliability, and own projects from technical design through production rollout. Participate in on-call support, observability, automation, and cross-team collaboration.

Design and deliver data architectures, cloud platforms, pipelines, integration frameworks, and governance practices for regulated-industry clients. Advise on data strategies, operating models, lakehouses, warehouses, data mesh, and AI-ready ecosystems. Lead transformation engagements, support pre-sales and thought leadership, mentor consultants and engineers, and maintain expertise in emerging data, cloud, analytics, and AI technologies.

Leads the design, development, testing, integration, and optimization of scalable data-processing and persistence components. Establishes engineering and operational standards, ensures pipeline performance and compliance, troubleshoots systems, supports proposals, and collaborates with architects, operations, security teams, customers, and managers. Coaches and develops engineers while overseeing code quality, data models, testing, deployment readiness, and technical delivery.

Build and evolve Azure-based data platform capabilities supporting data science, machine learning, and analytics at scale. Develop high-performance pipelines with Python, Scala, Spark, and Databricks; improve reliability, observability, data quality, and cost efficiency; create reusable engineering tools and patterns; contribute to architecture and technical standards; and mentor engineers across teams.

Build and scale governed data products, pipelines, analytical models, semantic layers, APIs, and AI-enabled interfaces. Establish standards for data modeling, orchestration, testing, observability, CI/CD, security, and lifecycle management. Partner with stakeholders to define data contracts and product roadmaps, deliver solutions end to end, improve analytics governance, and mentor colleagues. The role requires strong data and analytics engineering expertise, product judgment, and practical experience directing AI coding agents.