Top Analytics Engineer Jobs in London

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 data models, transformation workflows, pipelines, and integrations using SQL, dbt, and Python. Ensure data quality through automated testing, monitoring, and validation. Define standardized business metrics, optimize warehouse performance, document data systems, and collaborate with analytics, product, engineering, and business teams. Explore AI-powered tools to improve data workflows and prepare the company’s data foundation for AI-driven analytics.

Build and improve analytical data models supporting lending decisions, pricing, portfolio analysis, and investor reporting. Develop reliable, scalable ELT transformation pipelines using dbt and SQL, improve data quality and warehouse performance, troubleshoot analytics pipeline issues, and collaborate with analysts, engineers, product teams, and business stakeholders to strengthen the company’s analytical foundation.

Own the analytics transformation and semantic layers by building trusted dbt data models, BigQuery datasets, Looker dashboards, metrics, tests, and documentation. Translate stakeholder needs into scalable models and visualizations, optimize query performance and costs, improve data quality, and mentor junior analysts. Partner across commercial, product, marketing, finance, and operations to enable self-service analytics and consistent business reporting.

Leads the full lifecycle of data systems, ensuring data quality, accessibility, reliability, compliance, and infrastructure resilience. Partners with engineers, data scientists, product managers, DBAs, and stakeholders to define data needs, prioritize initiatives, develop roadmaps, and optimize media applications. Establishes KPIs, analyzes trends, promotes data literacy, and uses insights to guide product decisions and improve user experiences.

Develop and maintain analytical data models, datasets, semantic layers, and customer-facing reporting infrastructure. Lead data quality, observability, lineage, and model optimization initiatives while collaborating with data, software, and analytics engineers. Apply SQL and cloud data warehouse expertise, mentor junior engineers, and contribute to engineering best practices, including testing, documentation, version control, and continuous integration.

Owns analytics engineering end to end, building and maintaining dbt models, Omni dashboards, tests, documentation, and regulatory reports. Investigates data issues to their root causes, translates ambiguous business requests into trusted reporting solutions, and collaborates with stakeholders in a small data team. The role requires hands-on Omni, SQL, dbt, Git, and modern data stack experience, with hybrid attendance at a London office one to two days weekly.

Build and maintain scalable dbt data models, pipelines, macros, tests, documentation, and semantic layers. Develop trustworthy LLM-powered data transformations and agentic workflows with evaluation, monitoring, governance, and human oversight. Maintain analytics infrastructure supporting BI and natural-language querying, translate technical concepts for stakeholders, mentor team members, and collaborate with product, engineering, and BI teams.

Leads tax data engineering by gathering requirements, designing and operating scalable ETL/ELT pipelines, developing data models and warehouse structures, and ensuring data quality and observability. Partners with Tax, Analytics, Data Science, and business teams to validate data sources, support new initiatives, document systems, and automate recurring tasks using AI-assisted tools. The role also contributes to project planning, data tooling decisions, issue resolution, and engineering best-practice sharing.

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.

Own and improve NALA’s analytics transformation layer using dbt and Snowflake. Build governed, tested, documented, and agent-ready data models; manage streaming and batch pipelines; optimize warehouse performance and cost; establish CI/CD, monitoring, incident response, and engineering standards; deploy autonomous data agents; and mentor analysts. The role requires strong production experience with dbt, Python, semantic layers, streaming, orchestration, and deployment management.

Build and own ElevenLabs’ analytics transformation and metric layers using dbt and SQL. Define certified KPIs for product, finance, and company-wide reporting; establish modeling, testing, CI, documentation, and data-quality standards; collaborate with Data Engineering and stakeholders to create trusted, reusable data products and support AI-agent data use cases.

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.

Develops business intelligence and analytics solutions supporting underwriting, claims, and operations. Responsibilities include building Power BI dashboards, data models, SQL views, semantic layers, automated reporting, Databricks notebooks, data pipelines, and cloud analytics capabilities. The role also evaluates AI-enabled analytics use cases, improves self-service insight, governs reporting environments, and partners with stakeholders to translate business needs into scalable data solutions.

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 and enhance Neo4j’s Graph Data Science platform and Aura integrations. Build scalable Java, Go, and Python features bridging on-premises and cloud environments, improve cloud observability, optimize data transfer and streaming, and enhance developer experience. Collaborate across engineering teams to ensure interoperability with the core database while applying graph theory and research to solve customer problems.

Build and govern finance and revenue data models using dbt, SQL, and cloud data platforms. Own order-to-revenue pipelines, reconciliation, revenue recognition logic, testing, documentation, monitoring, and data quality. Collaborate with Finance, ERP integrators, analysts, and data engineers to create trusted, scalable data products and semantic layers. Help mature Airalo’s analytics engineering practices and support self-service analytics across finance, commercial, product, and marketing teams.

Build and scale governed data products, pipelines, analytical models, semantic layers, APIs, and AI-enabled interfaces. Establish standards for data modeling, orchestration, testing, observability, CI/CD, security, and lifecycle management. Partner with stakeholders to define data contracts and product roadmaps, deliver solutions end to end, improve analytics governance, and mentor colleagues. The role requires strong data and analytics engineering expertise, product judgment, and practical experience directing AI coding agents.

Serve as a customer-facing technical advisor for industrial analytics software. Design and implement scalable solutions, uncover high-value process-data use cases, guide customer adoption, troubleshoot integrations, mentor partners, and influence product roadmaps. Apply industrial process expertise to translate customer needs into technical solutions, collaborate with product and account teams, and communicate effectively with technical and executive stakeholders.

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.

Develop production-grade quantitative trading applications for equity derivatives desks. Responsibilities include building real-time pricing and curve-marking tools, reusable libraries, APIs, AI agents, backtesting capabilities, risk analytics, signal-generation systems, and desk workflow tooling. The role partners closely with traders, quants, sales, and technology teams to deliver cloud-hosted, mission-critical solutions supporting pricing, trading, and revenue generation.

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.

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.

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.