Top Data Engineer Jobs in London

Design and develop scalable reference-data services, automate lifecycle events, build/internal and external reporting (real-time and batch), collaborate with research and execution teams, maintain CI/CD and automated testing, and integrate Python and C# components to support trading and research workflows.

Design, develop, and optimize enterprise-scale Snowflake data warehouses, pipelines, models, queries, and workflows. Build ingestion and transformation processes with Python, PySpark, Airflow, and AWS. Ensure data quality, governance, security, compliance, performance, and cost efficiency. Automate deployment and monitoring through CI/CD and DevOps practices, troubleshoot production issues, and collaborate with stakeholders and engineering teams. Provide technical leadership, lead discussions, and mentor engineers.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

As a Senior Data Engineer at Simple Machines, you'll lead the design and build of cloud-native data platforms and pipelines, ensuring high performance and compliance while consulting directly with clients to deliver effective data solutions.

The Principal Engineer will shape Masabi's platform architecture, deliver scalable services, act as a technical leader, and navigate complex platform challenges. Responsibilities include design, delivery, influencing technical decisions, and contributing to technical documentation and forums.

Design and implement core YTsaurus functionality in C++, build and evolve platform-level capabilities, improve APIs and UX for internal users, own production quality including incident response/on-call, and collaborate with stakeholders to prioritize and deliver scalable data platform features.

Build and maintain production data pipelines and systems in Python; lead technical direction for the Insights platform; improve engineering standards, observability, and data quality; apply AI across products and pipelines; help develop AI-ready data foundations such as semantic layers.

As a Senior Data Platform Engineer at Depop, you'll architect and build data platforms, working with various teams to enhance data capabilities, support complex business problems, and promote self-service tools while ensuring operational excellence and effective stakeholder collaboration.

The Senior Data Engineer will deliver complex data engineering for insights and analytics, collaborating with multiple stakeholders and managing team outputs.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

Build and support large-scale data infrastructure and datasets for Spotify’s user behavior data. Design data architectures, develop collection and processing systems, and improve reliability, scalability, and usability. The role supports personalization, recommendation systems, and internal data teams while requiring collaboration across product and engineering disciplines.

Design, build, and evolve production-grade trading platforms and data-serving services. Optimize performance, throughput, and efficiency at scale while improving reliability, testing, automation, code quality, and observability. Participate in production support rotations, troubleshoot incidents, and drive operational improvements across distributed data systems.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Own and optimise the support queue and operational health of the data management platform. Investigate and resolve complex data pipeline and quality issues, lead client onboarding/configuration, coordinate cross-functional stakeholders, drive incident prevention and platform reliability, and mentor junior team members while ensuring data governance and secure user access.

Lead design and engineering for data centre projects, ensuring optimal performance. Manage teams, engage with clients, and mentor junior engineers.

The Principal Mechanical Engineer will lead mechanical engineering projects for data centres, managing multidisciplinary teams and contributing to strategic growth and thought leadership.

Build and operate GTM data systems covering account identity resolution, deduplication, corporate hierarchies, account and contact sourcing, enrichment, and Salesforce synchronization. Develop LLM-powered web research and enrichment pipelines, model data in dbt on Snowflake, orchestrate workflows with Airflow, and maintain quality controls. Partner closely with Sales, Marketing, RevOps, Partnerships, and Finance to create reliable data foundations that improve targeting, pipeline management, and revenue growth.

Develop stored procedures or notebooks to build 230 additional data items into DCoE tables/views. Create an extensible, config-driven process to add fields with minimal re-engineering. Produce self-documenting, well-commented code and collaborate with the DCoE team on delivery; DCoE handles business analysis, design sign-offs, data modeling, and testing.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

The Lead Data Engineer will develop and maintain a data platform, create data pipelines, ensure infrastructure support, and provide governance for stakeholders' data needs.

The Senior Data Engineer will design and build data pipelines on GCP, ensuring high data quality and reliability for analytics and ML products. Responsibilities include mastering ETL processes, managing workflows with Airflow, and collaborating closely with ML engineers.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Design, build and maintain ingestion pipelines and Databricks transformations; implement metadata-driven engineering, CI/CD, data testing and observability; evolve semantic models for reporting; lead and mentor a small engineering team and engage stakeholders to translate business needs into production-grade data products.

Drive business development for Victaulic within the UK data centre market by identifying major projects, engaging owners and engineers, expanding account penetration, coordinating with regional and international sales teams, documenting opportunities in Salesforce, and planning activities to meet sales targets. Promote products through stakeholder engagement, presentations, and industry association participation. Frequent regional travel and overnight stays required.

Design, build, and maintain large-scale distributed data systems and pipelines for blockchain analytics. Ship production code, drive technical direction, mentor engineers, improve reliability, observability, and cost efficiency, and collaborate with product, data science, and intelligence teams.