Top Analytics Engineer Jobs in London

The Analytics Engineer II will develop data products, optimize data schemas, build data pipelines, and collaborate with teams to improve data quality and insights.

Build, maintain and document tested, reusable datasets and data marts using dbt and cloud warehouses. Own daily operations, troubleshoot issues, perform code reviews, validate data, and collaborate with stakeholders to deliver analytics solutions and improve team practices.

Responsible for company-wide analytics, managing data accuracy, collaborating with product and engineering teams, and delivering actionable insights.

Build BI solutions and Looker dashboards for B2B products, analyze content licensing and contributor costs, automate cost reporting, collaborate with Product/Finance/Partnerships, and deliver partner-facing analytics and insights to optimize revenue and margins.

As a Data Analytics Engineer, you will design scalable data models, implement Looker structures, and optimize analytics workflows, ensuring data quality and operational efficiency.

Lead a team of developers to deliver high-quality quantitative analytics solutions for financial clients, focusing on API service-based and cloud implementations. Manage projects from inception to delivery, ensuring alignment with business strategies and enhancing performance through leadership and coaching.

Build and maintain data pipelines, create foundational data models, support data specialists, and ensure data quality. Collaborate with teams to optimize analytical solutions.

Design, build, and maintain a trusted semantic layer and reusable data models (dimensions, facts, metrics). Translate business logic into clear data specs, improve model reliability with testing and lineage, drive adoption across teams, standardize modelling conventions, and enable self-serve analytics for product, GTM, and finance stakeholders.

As a Senior Analytics Engineer, you'll model data, maintain reliability through tests, support teams with data insights, and ensure smooth fund flows to charities.

As a Senior Backend Engineer, you'll build scalable data systems for analytics, design distributed infrastructure on AWS, and deliver impactful customer-facing features. You'll drive technical innovation and collaborate across teams to optimize risk portfolios for insurers.

Design and maintain dbt data models and end-to-end pipelines, write complex SQL and macros, ensure data quality, build BI dashboards, and collaborate with analysts to maintain consistent metrics.

The Senior Analytics Engineer will lead technical direction, develop robust data models, and mentor team members to enhance data practices.

Collaborate with traders and researchers to design and build robust data systems for trading analysis in a fast-paced environment.

The Senior Analytics Engineer will design and optimize analytics architecture, manage data transformation, ensure data quality, and drive automation initiatives. Collaboration with cross-functional teams will enhance business intelligence capabilities.

Lead design and evolution of scalable data models and ETL/ELT pipelines, own Looker/LookML BI layer, ensure data quality/governance, collaborate with data science, and drive company-wide analytics strategy.

Own and uplift the transformation layer (dbt + Snowflake), maintain streaming and batch pipelines, enforce CI/CD, testing and documentation standards, optimize warehouse performance and costs, scope orchestration (dbt Cloud vs Dagster), and mentor analysts to enable AI-powered self-serve analytics.

The role involves designing and developing advanced pricing and risk platforms in financial markets, collaborating across teams, and promoting Agile methodologies while ensuring high-quality software delivery.

Lead and mentor data engineering teams to design, build, and optimize scalable ETL/ELT pipelines and data architectures on cloud platforms, ensuring data quality, performance, and alignment with business needs.

Design, build and maintain ETL/ELT pipelines and data models using Python, SQL, Spark and Azure. Ensure data quality, governance and performance, create dashboards, and work with cross-functional teams to deliver scalable data solutions.

The Analytics Engineer will design and maintain data models, collaborate with stakeholders for KPIs, ensure data quality, and create visualizations. They will optimize dbt models, write SQL queries, and improve data processes.

As a Platform Engineer, you'll build and operate distributed analytics platforms, collaborate with teams to enhance capabilities, and drive improvements in automation and observability.

Build and maintain Spotfire-driven analytics, dashboards, and automated reporting pipelines; integrate assay and portfolio data in BigQuery; develop metrics, predictive workflows, and agentic AI tools to inform next-best design decisions across the drug discovery lifecycle.

Lead analytics modelling and domain ownership: design and maintain dbt models, enforce governance, optimise Snowflake costs, build reliable Airflow orchestration, implement CI/CD and RBAC, mentor engineers, and partner with Product, Risk and Finance to deliver trusted analytics.

As a Data Engineer, you'll develop data pipelines, manage data integration and transformation, and support analytics and intelligence projects for government clients.