Top Data Engineer Jobs in London

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

Build and maintain Azure-based data pipelines, warehouses, Data Vault and dimensional data marts using ADF, Azure SQL, and T-SQL. Develop Power BI datasets, semantic models, and reports; optimize stored procedures; investigate source systems; and implement data quality monitoring. The role also involves profiling datasets, validating business results, debugging data platforms, and collaborating with clients and a remote European team.

Work in multidisciplinary teams to design, build and maintain data pipelines, models and analytics products using SQL, Python and modern data tools. Contribute to cloud-based data solutions (AWS/Azure), apply software engineering and data governance practices, create visualisations, and collaborate with stakeholders in Agile delivery to produce maintainable, production-ready data services.

Build and maintain reliable financial datasets for quantitative researchers and traders. Responsibilities include extracting, cleaning, aggregating, and onboarding data from diverse sources; developing robust extraction processes; investigating data quality issues; and improving onboarding tools. The role requires close collaboration with researchers, traders, engineers, management, and external data providers in a fast-paced quantitative investment environment.

Designs, builds, and maintains scalable AWS data platforms, ETL/ELT pipelines, data models, and processing workflows. Uses Python, SQL, infrastructure-as-code, CI/CD, monitoring, and data quality practices to deliver secure and resilient solutions. Collaborates with architects, security specialists, and clients; troubleshoots complex issues; establishes engineering standards; and mentors other engineers. The role also requires technical ownership and consideration of governance, access control, and regulated-environment requirements.

Own and expand third-party billing and usage integrations for a cloud cost platform. Build and maintain ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation, deduplication, and data quality checks. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve integration scalability. The role requires strong SQL and Python, cloud data warehouse experience, REST API integration expertise, and production pipeline ownership.

Own the paid media data domain end to end, including ingestion, user-level modeling, data marts, semantic definitions, reverse ETL, reliability, alerting, and incident response. Lead and mentor data engineers, establish technical standards, shape the roadmap, collaborate with platform and business teams, and remain hands-on in SQL and Python. Build production pipelines using dbt, Dagster, cloud warehouses, and related tooling while supporting analytics, machine learning, and data science use cases.

Perform hands-on data centre interventions across Sevenoaks and Maidstone, including equipment inspections, power cycles, cabling checks, KVM troubleshooting, media mounting, hardware control activation, and hot-swappable component replacement. The role requires precise execution of customer instructions, strong security awareness, customer communication, basic server and networking knowledge, shift flexibility, driving, and eligibility for SC security clearance.

Own end-to-end scientific data pipelines from ingestion through trusted, model-ready datasets. Model compounds, structures, assays, predictions, and experimental results while ensuring data quality, lineage, governance, and observability. Partner with ML engineers, data scientists, and researchers to build scalable solutions, shape data architecture, develop self-service and API capabilities, and mentor junior engineers.

Lead the design, deployment, and operation of scalable data pipelines and cloud infrastructure for Kpler’s marine insurance initiative. Build containerized AWS services using Python, Docker, Kubernetes, Airflow, and Terraform; manage PostgreSQL and Kafka/Flink streaming systems; establish observability standards; provide technical direction; collaborate cross-functionally; and mentor engineers.

Designs, builds, and operates secure cloud-native integrations and data platforms connecting internal systems, third-party platforms, correspondent banks, and data consumers. Responsibilities include Java and Spring Boot development, AWS infrastructure, event-driven data flows, Terraform, CI/CD, observability, data quality, and production support. The role contributes to architecture decisions, translates stakeholder requirements into technical solutions, mentors engineers, and promotes AI-assisted development practices.

Build and maintain cloud-native integration solutions connecting internal and third-party systems. Develop Java and Spring Boot services on AWS, write production code and tests, manage Terraform infrastructure and CI/CD pipelines, support observability and data quality monitoring, and participate in design and code reviews. The role also involves stakeholder collaboration, proactive issue resolution, and routine use of AI coding assistants and LLM-supported development practices.

Designs and implements data quality frameworks across batch and streaming pipelines on an AWS data platform. Responsibilities include validation checks, observability, anomaly detection, alerting, data contracts, schema validation, root cause analysis, quality SLAs, metadata management, lineage, and governance. Partners with data engineers, analysts, and business stakeholders to improve the reliability, discoverability, and trustworthiness of sportsbook data supporting analytics, trading, and machine learning.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

Sets technical direction for data and AI capabilities, leads large-scale production solutions, establishes engineering standards and reusable frameworks, improves AI monitoring and evaluation, partners with senior stakeholders, mentors engineers, and supports responsible AI adoption across the organization.

Lead development, maintenance, quality assurance, and operation of R-based numerical models for pEPR fees and payments. Own data strategy, dataset processes, reproducible analytical pipelines, automated testing, documentation, and version control. Analyze producer data, oversee the RPD portal, resolve technical issues with partners and regulators, communicate findings to non-technical audiences, and coordinate teams and stakeholders.

Build and launch scalable data pipelines, products, APIs, and tools for proprietary market research. Develop big data processing systems, reusable software components, AI-ready data assets, cloud infrastructure, and governance controls. Partner with investment professionals, researchers, data scientists, and Compliance to deliver reliable, auditable, compliant solutions while optimizing system performance, cost, and reliability.

Build Terrific’s monetization and commerce data products from the ground up. Responsibilities include designing event and identity models, consent and attribution systems, reconciling reporting data, creating advertiser-facing campaign and reporting tools, delivering sponsored placements, and integrating external buying platforms. The role combines hands-on backend and data engineering with product ownership, customer collaboration, technical specifications, build-versus-buy decisions, and independent judgment.

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.

Build, optimize, and maintain secure batch and streaming data pipelines using Snowflake and cloud platforms. Develop data models, support ingestion and transformation, apply governance and security practices, and collaborate with architects, engineers, analysts, and clients. The role also involves performance tuning, data solution delivery, technical leadership, mentoring engineers, and contributing reusable assets and best practices to the data engineering practice.

Designs and delivers scalable batch and streaming data pipelines using Databricks and Lakehouse architectures. Responsibilities include ETL/ELT development, data modeling, Delta Lake optimization, governance, security, orchestration, and cloud integration. The role is client-facing and involves collaborating with architects, engineers, ML teams, analysts, and senior stakeholders. It also requires mentoring engineers, improving data practices, enabling analytics and AI use cases, and contributing reusable assets and technical leadership.

Own and evolve Chip’s data platform architecture, including scalable pipelines, medallion architecture, data modeling, governance, security, and self-service enablement. Establish engineering standards, apply Databricks and Terraform best practices, optimize performance, and mentor data engineers. The role requires hands-on expertise in Databricks, Python, PySpark, SQL, databases, Git, infrastructure as code, and PII governance, with opportunities to re-architect the platform in a regulated fintech environment.

Lead large-scale technical initiatives and build scalable product functionality and infrastructure using TypeScript, React, and Node.js. Partner with product and design teams to develop privacy features for SaaS customers, make architectural tradeoffs, mentor engineers, support hiring, and establish engineering excellence across a growing organization.