Top Analytics Engineer Jobs in London

Lead Analytics Engineer mentors analysts, defines data infrastructure roadmap, builds and maintains core datasets and analytics pipelines, drives adoption of tooling (dbt, Airflow, BI), and enforces data modelling, monitoring, testing, and best practices.

The Lead Analytics Engineer at Graphcore is responsible for developing and managing the analytics engineering layer, creating robust datasets and metrics, and collaborating with business stakeholders to enhance decision-making through reliable analytics.

Design, build, and maintain ELT transformations and data models using dbt and cloud warehouses. Ensure data quality with tests and monitoring, optimise query and model performance, collaborate with analysts and engineers, and deliver scalable Looker visualisations while promoting modern data engineering practices.

Design, build, and maintain revenue data models and reconciliations across CRM, billing, and ERP. Deliver dashboards, forecasts, and analyses that explain revenue trends and support sales, finance, and executive decisions while improving data quality and integrity.

As a Senior Analytics Engineer, you will design models, optimize data pipelines, collaborate with teams, mentor juniors, and promote AI readiness.

Join the US Cards analytics foundation to build and improve dbt models and data transformations supporting credit decisions, origination, portfolio analysis, and reporting. Collaborate with analysts, engineers, and product teams to improve data quality, scalability, and the transformation pipeline while scaling infrastructure and learning SQL, dbt, and modern data stack tools.

Own marketing tracking and data pipelines end-to-end: implement and maintain GTM and Segment schemas, ensure reliable event flows into BigQuery and ad/CRM platforms, monitor and validate data quality, implement server-side conversions and hashed PII, ensure GDPR-compliant consent handling, and provide dashboards/alerts and documentation to support marketing decision-making and international expansion.

As a Senior Analytics Engineer, you'll design and maintain data models, create dashboards, and ensure data quality, working closely with various stakeholders.

The Analytics Engineer will manage analytics engineering relationships, develop data models and tools, and apply software engineering practices to optimize data-driven decisions.

As an Analytics Engineer II, you will build and maintain analytical data models, collaborate on data pipelines, and contribute to key metrics and dashboards to support decision-making.

Design, build and maintain dbt models and programmatic data quality rules. Partner with product, engineering, analysts and data scientists to measure and resolve data quality issues, perform root cause analysis, and champion data testing and governance best practices.

Build, maintain, and optimise the global data stack (Fivetran, Snowflake, dbt, Looker). Create tested dbt models and data marts, author custom Python pipelines on Azure, assemble complex datasets, implement CI/CD automation, and partner with cross-functional stakeholders to deliver high-quality, documented data.

Lead design and evolution of core analytical data models, define analytics engineering standards, build and optimize ETL/ELT pipelines, own the BI/semantic layer (Looker/LookML), drive data quality and governance, partner with cross-functional teams, and mentor analytics engineering across the company to enable reliable, scalable decision-making and experimentation.

Build and improve data models and analytic pipelines to support lending decisions, pricing, portfolio analysis, and reporting. Champion analytics engineering standards, collaborate with analysts and engineers, triage pipeline issues, and enhance reliability, scalability, and cost-effectiveness of the warehouse and transformation layer.

As a Senior Analytics Engineer, you'll design AI-augmented workflows, build scalable data models, and enhance data governance practices, while collaborating with cross-functional teams.

Build, maintain and deliver data models and assets in Snowflake. Collaborate with product, engineering and business stakeholders to translate requirements, uphold data governance, and support BI/analytics teams. Develop performant SQL transformations using modern tooling and ensure data quality, continuity, and alignment with product and engineering needs.

Support design, build, and maintenance of semantic models and curated datasets on the central data platform. Assist senior engineers with onboarding market datasets, data quality checks, documentation, and testing. Collaborate with BI and Data Science teams, follow modelling standards, and develop skills in SQL, data modelling, and platform tooling to enable BI and future AI use cases.

Support the US Cards analytics foundation by building and improving dbt models and ELT pipelines, contributing data models for credit and portfolio analysis, collaborating with analysts and engineers to improve data quality, scalability, and maintainability, and identifying pipeline efficiency improvements.

Build, test, and document dbt models in BigQuery to power reporting and BI. Partner with BI and backend teams, improve warehouse performance and costs, and contribute to data governance and semantic layers.

Lead the migration of reporting from SSRS to Snowflake, design scalable data models and semantic layers, build performant SQL transformations, introduce best practices (version control, CI/CD, testing), mentor BI developers, and collaborate with stakeholders to modernise reporting and governance.

Design and build a greenfield marketing data foundation: tracking/tagging, identity strategy, data pipelines, integrations (GA4, Salesforce, Databricks, COGNOS), data modeling and QA, dashboards, attribution/MMM, governance and compliance. Serve as primary contact for marketing data issues, enforce standards, prioritize analytics backlog, and enable stakeholders.

Contract role to build and maintain dbt models and analytics-ready datasets for financial services. Create reusable facts, dimensions, and marts; implement dbt tests, source freshness checks, and data quality validations; translate business requirements into data models and metric logic; document model definitions, lineage, and business rules; collaborate with analytics, product, and domain teams to ensure data fit for reporting and decisioning.

Build and own high-quality dbt data models, ensure performance, testing and documentation; collaborate with analysts and data scientists; promote best practices for modelling, transformation and governance; advise on data platform tooling and infrastructure; support AI solution architecture and upskilling across the business.

Lead design and governance of Borrowing's data layer: architect data products, build reusable feature layers, scale analytics engineering infrastructure, define SLAs/contracts, and partner across product, credit, engineering, and Data Platform to ensure consistent, production-grade datasets for analytics, ML, decisioning, and regulatory use.

Build and optimise downstream data pipelines and models (mostly in BigQuery) to support reporting, ML, financial and regulatory use cases. Define and apply data warehouse best practices, improve data quality, reduce complexity and cost, and support BI (Looker) and analytics consumers across the business.