Top Analytics Engineer Jobs in London

Build and operate Neo4j Aura’s Product Analytics Platform, including its Analytics API, event ingestion infrastructure, schemas, and integration contracts. Make architectural decisions for reliability and scalability, guide product teams through adoption, document designs through RFCs, and collaborate with Product Management and downstream data pipeline teams. The role focuses primarily on backend Go development, with some TypeScript or JavaScript and exposure to event-driven systems, observability, cloud platforms, and analytics tooling.

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.

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 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.

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 maintain reliable data warehouse models and pipelines to power GTM metrics, dashboards, and automation. Partner with Sales, CS, Product, and Engineering on instrumentation, CRM and usage modeling, data quality, observability, forecasting, and actionable data products for GTM teams.

The Staff Analytics Platform Engineer will lead the architecture and development of a scalable analytics platform, focusing on Snowflake, ingestion pipelines, and technical mentorship.

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

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.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Develop and enhance Neo4j Graph Analytics for Snowflake, focusing on SQL-facing APIs, build pipelines, and improving security while collaborating with clients.

Design and maintain scalable data models in Snowflake using dbt, expose datasets via BI tools, manage ingestion and data activation, enforce data quality and metadata, mentor junior engineers, and lead training to enable self-service analytics.

As a Senior Analytics Engineer, you'll design core data models, build AI system foundations, and ensure data quality while collaborating across teams.

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

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