Top Analytics Engineer Jobs in London

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.

Build and maintain data ingestion processes, APIs, data models, databases, and scalable pipelines using Python, Azure Data Factory, and Databricks. Automate analysis and reporting, support Power BI model migration, maintain data governance, troubleshoot issues, and collaborate with technical and business stakeholders. Provide user training, develop proof of concepts, and support carbon reporting and ecological transformation initiatives.

Design and build reliable data pipelines and data products using dbt and Databricks. Define modelling standards, testing, documentation, and observability. Collaborate with stakeholders, resolve production incidents, automate processes, and lead data initiatives end-to-end to support analytics and CRM use cases.

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.

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 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.

Lead technical strategy for enterprise analytical data modelling, analytics automation and engineering empowerment. Build reusable modelling patterns, automation (including Snowflake Cortex and LLMs), self-service frameworks, mentor engineers, and drive platform modernisation to enable squads to deliver scalable, trusted Data Products.

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.

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.

Design, build and maintain scalable ETL/feature pipelines to transform telematics sensor data into analytics-ready models. Own datasets end-to-end (testing, reliability, documentation), optimise query performance in cloud warehouses, and support feature engineering for pricing, underwriting and data science teams using Snowflake/Azure and dbt.

Design, build and maintain scalable telematics data pipelines and dbt models in a cloud data platform. Create productionised features for pricing and modelling, ensure data quality, testing, documentation, and performance optimisation while collaborating with data engineers, analysts and data scientists.

Design, build, and own end-to-end analytics data products and pipelines using dbt, BigQuery, and Airflow. Develop performant, cost-conscious ETL, automate testing and validation with Python, integrate ingestion tools, and create BI visualisations while maintaining standards and data quality.

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.

Lead the analytics modelling layer: own dbt models, semantic layer, and data products to enable self-serve analytics. Establish best practices, review code, ensure data quality and performance, partner with data scientists and business stakeholders, and apply AI tools to accelerate development and governance. Technical leadership without direct reports, influencing product, marketing, finance, and content decisions.

Develop, test, and support reliable, high-performance distributed Java applications for real-time streaming and concurrent workloads. Partner with stakeholders, present and review designs, improve reliability/observability/performance, and support production releases to meet SLAs within BGM Data & Analytics.

Build and maintain scalable dbt models on Databricks to create reusable data products powering reporting, self-serve and conversational analytics. Ensure data quality via tests and documentation, optimise performance, collaborate with analysts and stakeholders, participate in code reviews, and evolve platform capabilities across subscriptions and content domains.

Build and maintain data pipelines and ETL, transform and model data with SQL and dbt, and use Python to build workflows, automation, and tools to optimise analytics products while collaborating with technical and non-technical stakeholders.

Work at the intersection of analytics engineering and Python-based data engineering to transform and model data using SQL and dbt, build and maintain data pipelines/ETL, automate workflows with Python, and deliver high-quality data products while collaborating with technical and non-technical stakeholders.

Build and maintain trusted domain models from raw data to semantic layer. Define analytics engineering standards, extend dbt/warehouse and semantic tooling, integrate new data sources, partner cross-functionally, and deliver models that power commercial, financial, risk, and operational decisions.

Design and build reliable data models and exposures using dbt and Snowflake, leveraging AI-assisted workflows. Own data quality, tests, contracts, and observability. Integrate third-party sources, create secure data shares, enable stakeholders with self-service BI, and continuously improve platform tooling and AI-driven processes.

Implement, configure and debug web and mobile tracking and MarTech systems (Tealium, GA4, MMPs). Ensure data quality, privacy/compliance, QA and documentation while partnering with MarTech, CRM and Product teams.

Design, build, and maintain scalable analytics models and automated reporting. Partner with Sales, Product, Marketing, and Finance to define KPIs, deliver ad hoc analysis, improve data quality and observability, and establish analytics engineering best practices to enable self-service analytics.

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