Top Data Engineer Jobs in London

Lead customer-facing delivery of production-grade data and AI solutions using Databricks. Own architecture and design decisions, build end-to-end data pipelines, applications, and ML/AI integrations, and guide enterprise customers through implementation. Collaborate with engineering, product, project management, and support teams; manage technical scope and outcomes; provide training and documentation; and develop reusable frameworks and best practices. Travel to customers approximately 20% of the time.

Lead the design, development, optimization, and operation of scalable data pipelines, ETL/ELT processes, data platforms, and analytical data models. Translate data architecture into production solutions, ensure data quality and performance, optimize infrastructure costs, and support regulatory reporting. Provide hands-on coding, infrastructure, troubleshooting, code reviews, and technical leadership while mentoring junior and mid-level engineers and collaborating with architects, product managers, data scientists, and business stakeholders.

Designs, builds, and maintains dimensional data models and quality-assured analytical datasets using dbt. Integrates complex data sources, manages metadata, performs data profiling, and supports data quality and governance standards. The role involves gathering requirements, collaborating with data engineers and analysts, documenting models, and enabling tools such as Airflow. It requires strong stakeholder communication and experience delivering analytics engineering solutions in complex or public-sector environments.

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.

Lead a small data engineering team while remaining hands-on building and operating production pipelines, integrations, data services, and platform capabilities. Own the data roadmap, reliability, governance, security, monitoring, and quality practices. Partner with Product, Engineering, Data Science, and Insights, while mentoring engineers, managing delivery, and using AI-assisted development tools responsibly.

Designs and operates data-protection controls across systems, products, endpoints, email, and cloud stores. Responsibilities include data discovery and classification, loss prevention, encryption, masking, secure disposal, privacy automation, threat modeling, incident investigation, digital evidence handling, and security metrics. The role serves as a technical authority, leading cross-functional privacy and security reviews and translating risks into reusable engineering controls, standards, guardrails, and policy automation.

Develop statistical and machine learning sports models, engineer data assets, implement pricing engines in Python, and integrate solutions into Sportsbook applications. Build automated tests, validate data flows, support model deployment and lifecycle management, collaborate with Trading, Product, Engineering, and QA, and mentor junior data scientists.

Build and deliver production-grade AI and GenAI solutions for enterprise clients, including agentic systems, RAG pipelines, automation workflows, and model orchestration layers. Lead AI transformation engagements from strategy and architecture through deployment and scaling. Support presales, thought leadership, proposition development, mentoring, and technical capability building while advising clients on AI architecture, evaluation, cost, latency, scalability, and responsible AI.

Build, scale, and operate Databricks and AWS infrastructure supporting automated, reproducible machine learning workflows. The role includes infrastructure-as-code with Terraform or Pulumi, containerized platforms using Docker and Kubernetes, system architecture, reliability and cost optimization, and cross-functional collaboration with data and ML teams. The engineer will lead complex projects, improve platform scalability and usability, document systems, and mentor peers while operating production-scale data and ML platforms.

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.

Designs and delivers enterprise data solutions using Microsoft Fabric, from discovery and prototyping through architecture and implementation. Builds data products and platforms supporting reporting, analytics, and AI using pipelines, notebooks, lakehouses, and warehouses. The role involves SQL and Python development, architectural decision-making, customer engagement, mentoring colleagues, and contributing to the data and AI community. Familiarity with capacity management, metadata-driven platforms, data modeling, Git, AI-driven development, Azure DevOps, and infrastructure is expected or desirable.

Build and operate a centralized game event data hub by developing data ingestion, preparation, orchestration, quality checks, backfills, metadata management, monitoring, and alerting. Collaborate with data engineers across studios to align shared solutions with existing patterns. Contribute to productionizing, monitoring, and maintaining machine learning pipelines as responsibilities expand.

Build and operate scalable, multi-tenant data infrastructure for millions of Lovable applications. Responsibilities include ingestion, analytical storage, schema management, partitioning, compaction, query performance, isolated agent-written connectors, scheduling, retries, metering, workload isolation, observability, freshness, and recovery. The role requires production ownership across data platform layers while controlling tenant costs and protecting workloads from untrusted or noisy producers.

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.

Build and operate a production Azure data and AI platform. Responsibilities include Azure landing zones, network architecture, identity, governance, monitoring, Databricks and Unity Catalog administration, data integrations, MLOps pipelines, model deployment, CI/CD, infrastructure as code, security scanning, self-service tooling, documentation, and on-call support. The role requires hands-on Terraform, Azure, Databricks, containerization, observability, and Python or PowerShell experience.

Build and operate an Azure-based data and AI platform through hands-on infrastructure engineering. Responsibilities include designing Azure landing zones and networks, managing governance and identity, deploying Databricks and data services, building MLOps pipelines, implementing DevSecOps practices, creating self-service tooling, and supporting platform incidents. The role requires Terraform, Azure, Databricks, CI/CD, containerization, security, observability, and Python or PowerShell expertise.

Designs, builds, and operates secure, scalable GCP data platforms for financial-sector clients. Develops batch and streaming pipelines, architects data lake and warehouse solutions, manages infrastructure through Terraform and CI/CD, and implements governance, security, monitoring, and data quality practices. Collaborates with stakeholders, communicates architecture decisions, mentors engineers, and promotes DataOps and engineering standards.

Lead technical design, delivery, governance, and operation of cloud-native data and analytics products on GCP. Provide DevOps and data engineering leadership, implement secure scalable architectures, CI/CD, infrastructure as code, observability, data integrations, and compliance controls. Remain hands-on in solution design, coding, reviews, and complex problem-solving while mentoring engineers, shaping technical strategy, partnering with stakeholders, and driving continuous improvement.

Lead the technical direction, architecture, quality, security, governance, and operational excellence of Funding Circle’s data platform. Own Snowflake infrastructure, ingestion, orchestration, CI/CD, observability, cost controls, and the Athena-to-Snowflake migration. Mentor engineers, establish reusable standards, support data teams, manage vendors, document technical decisions, and guide safe AI-assisted engineering practices.

Build and operate Spotify’s lakehouse and BigQuery platforms at scale. Develop backend services supporting Apache Iceberg, migrate pipelines, manage BigQuery capacity, create monitoring and tooling, and troubleshoot production issues across storage, Kubernetes, IAM, and processing systems. Participate in support and on-call rotations while improving reliability, cost efficiency, and user enablement across global teams.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Own production Python data pipelines, ETL/ELT workflows, and the ontology powering an AI platform. Integrate warehouses and business applications with graph databases and vector stores, while managing deployment tooling, cloud infrastructure, monitoring, and security. The role begins as hands-on individual contribution and develops toward technical leadership through mentoring and architecture ownership.

Leads tax data engineering by gathering requirements, designing and operating scalable ETL/ELT pipelines, developing data models and warehouse structures, and ensuring data quality and observability. Partners with Tax, Analytics, Data Science, and business teams to validate data sources, support new initiatives, document systems, and automate recurring tasks using AI-assisted tools. The role also contributes to project planning, data tooling decisions, issue resolution, and engineering best-practice sharing.