Top Analytics Engineer Jobs in London

Build and own Ohme’s scalable analytics platform, including Snowflake, dbt data models, semantic layers, data quality, observability, security, and cost optimization. Develop reliable, documented analytics assets, establish engineering standards, support analysts and business users, and use AI tooling to improve team efficiency. The role requires strong SQL, dimensional modeling, stakeholder communication, and ownership of warehouse reliability and performance.

Build and maintain scalable dbt data models and pipelines supporting analytics and data science. Translate business requirements into dimensional warehouse schemas, define trusted metrics, implement testing and documentation, optimize SQL and models, and contribute to semantic and BI layers for AI-driven self-service. Use AI coding assistants responsibly, review generated code, collaborate through GitHub and CI/CD, and work with Snowflake and data lake architecture.

Build and improve analytical data models supporting lending decisions, pricing, portfolio analysis, and investor reporting. Develop reliable ELT pipelines and transformation workflows using the company’s modern data stack. Collaborate with analysts, product teams, engineers, and stakeholders to translate requirements into robust datasets, resolve pipeline issues, improve data quality, and strengthen analytics engineering standards, scalability, maintainability, and cost-effectiveness.

Own product analytics across product lines by defining success metrics, analyzing user behavior and retention, supporting experimentation, and guiding product strategy. Build scalable data products, governed self-service analytics, and event taxonomies using SQL and dbt. Partner with Product, Engineering, Design, and Go-to-Market teams to connect product usage with commercial outcomes. Promote data literacy and apply AI and LLM tools to improve analytics workflows.

Build behavioural data models, analytics pipelines, event contracts, semantic models, and data quality controls in Databricks. Partner with product and engineering teams on frontend instrumentation, experimentation tracking, identity resolution, and trusted metrics for reporting, self-service analysis, and AI-assisted workflows.

Build and operate high-throughput trading data pipelines, analytics engines, time-series models, and analytical databases. Develop internal AI platforms, RAG pipelines, prompt-management tools, and controllable agent systems. Optimize systems for reliability, performance, data quality, reconciliation, monitoring, and cost. Collaborate with quantitative trading teams using financial and cryptocurrency datasets, market data, and trading metrics such as PnL, risk, balances, and trade activity.

Designs and maintains reusable data warehouse models, dimensional structures, SQL transformations, stored procedures, views, and data-loading processes. Investigates complex source systems, reconciles data, resolves quality issues, documents logic, and supports BI and Power BI requirements. Owns work through development, testing, deployment, and maintenance while collaborating with stakeholders to deliver reliable, scalable analytics assets.

Develop and maintain realtime analytics, monitoring, and trading control systems for a large algorithmic trading platform. Responsibilities include ingesting high-volume trading data, building data-processing and in-memory query frameworks, presenting metrics, supporting trading controls, ensuring 24/7 reliability, collaborating with front-office users and frontend developers, and participating in on-call support.

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.

Implements and maintains digital and product analytics tracking across mobile apps and websites. Translates business and stakeholder requirements into measurement specifications, configures MarTech tags, and partners with engineering teams on implementation, QA, UAT, and release monitoring. Supports data governance, troubleshooting, documentation, analytics debt reduction, and marketing technology delivery using Google Analytics 4, Google Tag Manager, Firebase, and Segment.

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.

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.

Build and maintain trusted data models and assets in Snowflake, collaborate with business and technical stakeholders, uphold data governance, and support BI and Analytics teams. The role requires performant SQL, transformation tooling, structured and unstructured data modeling, data quality, testing, version control, and orchestration experience. It supports data operations and business continuity within a fast-paced fintech environment.

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.

Own analytical domains end to end by defining trusted metrics, dimensions, and business logic in semantic models; building production dbt data models; and delivering reliable Tableau reporting. Partner with operations, commercial, finance, and supply chain stakeholders, validate governed datasets for AI consumption, reduce conflicting outputs, promote data literacy, review modeling work, and train stakeholders on new capabilities.

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 operate core components of a distributed, multi-tenant database and SaaS data platform, including ingestion, query planning, distributed execution, storage formats, APIs, tenant isolation, quotas, audit logs, and background jobs. Develop agent-facing retrieval interfaces, instrument services with observability tooling, and shape architecture as the product evolves from prototype to production. Collaborate across product and infrastructure while making pragmatic decisions in an autonomous environment.

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.

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.