Top Analytics Engineer Jobs in London

Design and own dbt-based transformation layers that convert raw operational data into reliable, documented analytical assets. Build tested data models, semantic layers, metrics, and entities supporting dashboards and self-service analytics. Collaborate with engineers, analysts, and stakeholders to translate requirements into production data products. Use SQL, Python, Git, CI/CD, and AI coding tools to accelerate delivery while maintaining testing and human oversight. Review peer work and maintain models through schema changes and user feedback.

Build and maintain scalable data models and curated datasets for analytics, BI, product, and data science. Apply consistent business logic, define metrics, implement automated data quality tests, and document data assets. Partner with Data Engineering, Analytics, Product, Data Science, and commercial stakeholders to align KPIs and support decision-making. Use Snowflake, dbt, Airflow, Python, SQL, cloud services, Git, CI/CD, and Agile practices.

Build and improve analytical data models supporting lending decisions, pricing, portfolio analysis, and investor reporting. Develop scalable ELT transformations, strengthen data quality and reliability, support analysts, troubleshoot pipeline issues, and improve transformation efficiency and cost-effectiveness. Collaborate with product, engineering, analytics, and business stakeholders while promoting analytics engineering standards and effective use of AI tools.

Designs and maintains semantic models, curated datasets, and reusable data assets on Databricks. Translates business questions into scalable data models, scoring frameworks, transformation metrics, and business-case analyses. Collaborates with data engineering and business stakeholders, supports AI and agentic workflows, monitors data quality and performance, and promotes governance, documentation, standards, and peer review.

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.

Build and maintain dbt-based ELT pipelines and scalable data models in Snowflake or similar cloud warehouses. Ensure data quality through testing, monitoring, documentation, lineage, and CI/CD practices. Optimize SQL queries and warehouse performance, develop Looker visualizations, and collaborate with data engineers, analysts, and business users. Champion modern data-stack adoption, with preferred experience in Python, semantic layers, Airflow, and software engineering practices for data.

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 metric definitions, KPIs, source-of-truth tables, and data models using advanced SQL and dbt. Own dbt Cloud infrastructure, optimize models and queries, manage GitHub workflows, and create dashboards with BI tools. Partner with analysts, data engineers, and business stakeholders to solve problems, deliver insights, and improve self-service analytics through Snowflake Intelligence. Requires strong communication, organization, stakeholder management, and understanding of sports betting or customer behavior.

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.

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 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.

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.

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.

Founding analytics engineer for Finance to design and own data infrastructure, build production ETL/ELT pipelines, create scalable data models, ensure data quality and testing, and deliver actionable insights to Finance leadership to support month-end close, capital allocation, and IPO readiness.

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 enhance digital products for Global’s media and entertainment brands. The role involves using cloud computing, serverless technologies, algorithms, microservices, and containerization in an agile environment. Successful candidates communicate well, think creatively, work independently and collaboratively, maintain high-quality standards, and demonstrate enthusiasm for learning and solving problems with new technology.

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 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.

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.

Own dbt transformation layers, dimensional models, reusable business metrics, and semantic layers for SaaS data. Integrate product events, Salesforce CRM, and support data while partnering with data engineers on schemas and contracts. Establish data quality testing, documentation, monitoring, and lineage practices. Translate ambiguous business questions into reliable data assets supporting analytics, product, and AI use cases.

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.