Top Analytics Engineer Jobs in London

Own and improve finance data models, revenue and cost reporting, management accounts, budgeting, forecasting, reconciliations, dashboards, and data controls. Build dbt models and Python services, map financial data sources, support new products and markets, investigate anomalies, and explain business performance to Finance and Engineering stakeholders. The role requires strong SQL, Python, finance fundamentals, and experience with data transformation and version control.

Build and maintain analytics software across the full development lifecycle. Design scalable services, APIs, data models, and AI-powered analytics features such as conversational analytics and natural-language-to-SQL. Collaborate with engineers, product managers, and analysts to translate business needs into reliable solutions. Diagnose technical issues, improve performance and observability, integrate AI/ML capabilities safely, and rapidly develop reusable self-service products.

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.

Build and improve analytical data models supporting lending decisions, pricing, portfolio analysis, and investor reporting. Develop reliable, scalable ELT transformations, strengthen data quality and warehouse usability, support analysts, troubleshoot pipeline issues, and promote analytics engineering standards. Collaborate with technical and non-technical stakeholders while using modern tools including SQL, Snowflake, dbt, Fivetran, and AI.

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.

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.

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 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.

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.

Build production-grade quantitative trading applications and services for equity derivatives desks. Responsibilities include developing real-time pricing and curve-marking tools, reusable libraries, APIs, AI agents, and desk analytics supporting pricing, signal generation, backtesting, risk analysis, and market insight. Collaborate directly with traders, quantitative analysts, Sales, and Trading teams from concept through production.

Develop and manage Microsoft Fabric and Power BI platforms, including data pipelines, lakehouses, semantic models, reporting, integrations, governance, and automation. Support internal applications and enable Microsoft Copilot and Azure AI capabilities. Partner with business teams to translate requirements into scalable technical solutions while establishing standards for data quality, security, self-service analytics, and enterprise data governance.

Build and maintain scalable data models and transformations in Snowflake using advanced SQL, Coalesce, and/or dbt. Develop structured and unstructured data models, optimize queries, support data pipelines and BI reporting, and uphold data quality and governance standards. Collaborate with Product, Engineering, BI, Analytics, and business stakeholders to deliver reliable, business-ready data assets and improve data modeling practices.

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.

Leads complex analytics engineering workstreams and builds scalable data models and solutions using SQL, dbt, Python, and modern data platforms. Drives data quality, testing, CI/CD, documentation, performance, and operational excellence. Partners with cross-functional teams to deliver trusted data products, reporting, and commercial insights. Owns data incidents, mentors engineers, influences technical strategy, and establishes engineering standards across the organization.

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.

Lead complex data and analytics workstreams while designing scalable solutions with SQL, dbt, Python, and modern data platforms. Drive data modeling, testing, CI/CD, documentation, reliability, and incident resolution. Partner with Product, Analytics, Technology, and Data Platform teams to deliver trusted data products, reporting, and commercial insights. Mentor engineers, delegate effectively, influence stakeholders, and help shape technical strategy and roadmaps.

Develop and optimize SQL transformations and dbt projects on Snowflake, administer dbt Cloud workflows, and maintain scalable analytics infrastructure. Manage semantic layers in Omni, data observability, GitHub workflows, automated testing, and data quality processes. Collaborate with data engineering teams to integrate ingestion and transformation layers, establish coding standards, and support governed metrics and AI-ready semantic context.

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 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.

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.