Top Analytics Engineer Jobs in London

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.

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.

Lead data modelling strategy and standards, design and implement data models (star schemas), build scalable transformations using SQL, dbt, Python/PySpark, mentor engineers, co-own backlog, and translate business needs into technical solutions including custom AI and automation.

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.

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.

The Analytics Engineer will build data models, support financial functions, and collaborate with finance and crypto engineering teams to enhance data operations.

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.

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 Data Analyst, you'll analyze data, build dashboards, identify trends, support decision-making, and contribute to modeling and experimentation in a fintech setting.

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.

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.

Build and scale dbt projects and CI/CD, design performant data models, enforce data quality/PII governance, partner with commercial and product teams, and enable BI self-service via Lightdash to power internal and customer-facing analytics.

Design, build and maintain DBT models, transformation pipelines and analytics workflows. Create BI dashboards, implement data quality checks, optimise data models, support platform migrations, and collaborate with stakeholders to deliver reliable, scalable analytics solutions.

Lead a team of analytics engineers to deliver AI-enabled data products on the LEGO Data Platform (Databricks). Own delivery, CI/CD, data quality, Unity Catalog governance, collaboration with commercial stakeholders, coach junior engineers, and drive data accessibility, performance and cost optimisation.

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.

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.

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.

Own and improve analytical foundations for US Loans: design and maintain data models, drive dbt transformations, mentor analysts, bridge product/backend/platform teams, and optimise ETL/ELT pipelines for reliability, scalability, and cost-effectiveness to accelerate insight generation.

Build and own high-quality dbt data models; collaborate with analysts and data scientists to enable insights; promote best practices for modelling, transformation, and governance; consult on data platform tooling and infrastructure; design maintainable AI-driven solutions and mentor colleagues in advanced analytics.

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.

The Analytics Engineer will transform raw data into insights, manage the data stack, automate processes, and engage with stakeholders to optimize data usage.

The Senior Analytics Engineer will build and maintain data products and infrastructure, collaborate with teams, mentor other engineers, and enhance data-driven decision making across the organization.