Top Data Jobs in London

Partner with Product, Design, Engineering and Analytics Engineering to analyze customer behaviour, define KPIs, run funnel/cohort analyses, design and evaluate A/B tests, build dashboards, and surface actionable product insights to drive activation, engagement and retention.

Lead data engineering for the Flexibility team by establishing AWS-based data systems, scalable processing and warehousing, platform improvements, and architectural standards. Build foundational data capabilities, maintain critical applications, integrate with Databricks, improve operational reliability, and support global scalability. The role also involves mentoring backend engineers, defining requirements, documenting solutions, visualizing data, and collaborating with the broader data community.

Own the end-to-end product lifecycle for Sustainable Investment (SI) data platforms: define roadmap; onboard and operationalize new data sources; lead migration from legacy systems to cloud/lakehouse/warehouse platforms; partner with engineering, operations, and commercial teams; and drive data quality, coverage, timeliness, and regulatory/client compliance.

Leads data analytics consulting projects, develops global analytics and technology offerings, and provides thought leadership for regulatory, compliance, investigations, and business matters. Designs data models, dashboards, statistical analyses, and risk-based methodologies; manages client requirements, proposals, budgets, timelines, and delivery. Supports business development, mentors junior staff, communicates with stakeholders, and advises on complex, multi-jurisdictional engagements. International travel may be required.

Lead EMEA debt capital raising legal work, including acquisition, construction, securitization, leveraged, project, and real estate financings. Negotiate transaction documents, monitor covenant and post-closing compliance, manage external counsel, advise on amendments, waivers, M&A financing issues, and lender consents, and improve legal and compliance processes. Partner with Finance, Treasury, Tax, Development, Power, and senior executives to manage risk and support data center business objectives across jurisdictions.

Build and steward Databricks lakehouse solutions across EMEA and APAC, including agentic workflows, AI governance, semantic models, data models, and automated ingestion pipelines. Troubleshoot production failures, optimize performance, resolve data quality issues, and improve reporting. The role requires advanced SQL, Python, PySpark, Databricks, Azure, data modeling, and systems thinking, with opportunities to work on geospatial processing, master data management, and AI-amplified engineering practices.

Designs, builds, and maintains scalable AWS data platforms, ETL/ELT pipelines, data models, and processing workflows. Uses Python, SQL, infrastructure-as-code, CI/CD, monitoring, and data quality practices to deliver secure and resilient solutions. Collaborates with architects, security specialists, and clients; troubleshoots complex issues; establishes engineering standards; and mentors other engineers. The role also requires technical ownership and consideration of governance, access control, and regulated-environment requirements.

Manage and enhance customer insight data models, maintain data integrity, collaborate on internal and third-party data requirements, and produce regulatory metrics, thresholds, dashboards, and management information. Analyze customer outcomes, identify root causes, generate actionable insights, raise queries with business owners, and support compliance with FCA conduct and consumer duty requirements. The role partners with cross-functional teams to improve customer-centric decision-making and evidential reporting.

Own data strategy and experimentation for a critical product domain, influencing senior leadership decisions through causal inference, forecasting, LTV modeling, retention analysis, pricing analysis, and scenario planning. Define key metrics, lead strategic analytical initiatives, improve measurement frameworks, and mentor senior data scientists while partnering with Product, Engineering, and Business leaders.

Develop and optimize machine learning and generative AI solutions for digital agents, including LLMs, NLP, RAG, retrieval, experimentation, evaluation, and statistical analysis. Build scalable data and ML pipelines using Python, SQL, and Spark; assess model quality, reliability, safety, and business impact; and support production deployment. Collaborate with engineering, product, and data science teams while mentoring less experienced colleagues.

Create and present short-form football content as the face of Compare the Market’s EFL partnership. Attend EFL matches nationwide, engage fans, manage community interaction, and showcase club traditions through a recurring social media series. The role requires regular travel, confident on-camera presenting, strong editorial judgment, brand alignment, and the ability to work within approval processes while maintaining an authentic voice.

Manage and optimize high-load Microsoft SQL Server databases, including advanced T-SQL development, query and execution-plan optimization, indexing, locking, memory, disk performance, AlwaysOn availability, maintenance, recovery, security, integrity checks, monitoring, and incident resolution. Support analysts and ML engineers with reliable data access across databases reaching tens of terabytes.

As a Commercial Strategy Lead, you will shape and drive commercial strategies, develop go-to-market approaches, and lead cross-functional initiatives to unlock new growth opportunities.

Lead data integration activities for Kinaxis solution deployments, including data mapping, transformation, loading, testing, validation, system configuration, and deployment. Design integration solutions, define requirements, support customer workshops and training, and collaborate with architects, support, and cloud teams. Advise customers on integrating Kinaxis Maestro with enterprise systems using ETL tools, APIs, databases, and ERP connections. The role is remote in the UK and may require up to 70% travel.

Architect and implement scalable AWS data warehouses, data lakes, lakehouses, and resilient data pipelines. Lead solutions using AWS analytics, storage, processing, integration, governance, orchestration, and database services. Develop with Python, PySpark, and SQL; optimize schemas, queries, partitioning, indexing, and performance. Support legacy and on-premises migrations to AWS, collaborate with globally distributed teams, and contribute to enterprise application workloads and financial-services use cases.

Own end-to-end data science and analytics projects for global business operations. Analyze performance against goals, investigate deviations, build scalable pipelines, models, dashboards, monitoring, and investigative tools, and measure impact through experimentation and causal inference. Translate complex findings into actionable recommendations for senior stakeholders while collaborating across international markets, Data Science, Operations, Commercial, and Finance teams.

Leads global data acquisition for fraud and identity products, focusing on mobile identity and phone intelligence. Manages strategic data supplier relationships, evaluates and onboards external data assets, negotiates contracts, oversees governance and performance, and partners with product, legal, compliance, finance, analytics, and technology teams. Develops business cases and ensures acquisition activities meet regulatory, risk, operational, and commercial requirements.

Support the development, testing, evaluation, and productionization of AI/ML models and applications, including LLMs, agentic systems, document AI, forecasting, and optimization. Collaborate with engineering, product, business, and client stakeholders to translate requirements into technical work, establish performance metrics, maintain governance documentation, and monitor production systems for accuracy, cost, latency, and business impact.

Designs and maintains scalable, high-performance data pipelines and infrastructure for real-time analytics. Owns the Cube.js semantic layer, integrates Salesforce and customer data sources, leads dbt transformation workflows, and manages Apache Airflow orchestration. The role focuses on low-latency processing, data quality, observability, and effective data modeling. It also involves collaborating with architects and business stakeholders, mentoring junior engineers, and delivering complex data initiatives end to end.

Own AI safety datasets from collection through labeling, quality control, versioning, and training readiness. Translate safety policies into consistent labeling and evaluation criteria, manage external contributors, and build production model evaluation workflows. Develop lightweight Python and SQL pipelines for reproducible data analysis, partnering with safety researchers, policy specialists, and ML engineers. The role also contributes to scalable dataset methodologies, quality standards, and human-in-the-loop processes.

Build and operate Python-based tools, data pipelines, analytical models, dashboards, and AI/ML solutions for Finance. Partner with Finance users to automate reporting, reconciliations, controls, and workflows. Deploy solutions on Azure using secure, governed software engineering practices, CI/CD, monitoring, and documentation. Collaborate with Data Engineering, Technology, and Risk teams while coaching Finance colleagues on Python, automation, and data tooling.

Own and expand third-party billing and usage integrations for a cloud cost platform. Build and maintain ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation, deduplication, and data quality checks. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve integration scalability. The role requires strong SQL and Python, cloud data warehouse experience, REST API integration expertise, and production pipeline ownership.

Develop, optimize, deploy, and monitor statistical and machine learning models for commercial lending products. Analyze alternative data sources, experiment with model optimization frameworks, identify risks, and communicate technical findings as commercial insights. Collaborate with data engineers, analysts, and business stakeholders while using Python, SQL, and AI techniques to solve complex business problems and improve borrower products.

Analyze user, product, and business data to identify improvement opportunities and guide strategic decisions. Design and evaluate A/B tests, build dashboards and reports, communicate insights to cross-functional stakeholders, and use AI and automation to improve analytical workflows. The role partners with product, engineering, and marketing teams in a consumer digital product environment.

Support building and maintaining data pipelines, onboard and transform partner data, monitor reliability, validate data quality, automate processes, and document pipeline logic to ensure trustworthy datasets for the business.