Top Data Analyst Jobs in London

Analyze large fan and marketing datasets using SQL, Python, Databricks, AWS, and Snowflake. Build dashboards, recurring reports, audience segments, and campaign performance tracking for international markets. Translate business questions into actionable insights, support marketing campaigns and UAT, investigate data issues, and collaborate with marketing, data engineering, international teams, and external partners. Clearly communicate findings to nontechnical stakeholders while supporting fan growth and engagement analytics.

Leads the People Analytics data function, managing and developing analysts while delivering dashboards, data products, and complex workforce insights. Translates HR requirements into scalable technical solutions, oversees data quality and governance, defines tooling and technical strategy, and partners with HR, Finance, HRIS, and IT stakeholders. Performs hands-on analysis of complex HR datasets, including attrition, workforce trends, and diversity, equity, and inclusion.

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

Supports the Head of Controls and Controls Testing Team by using data analytics to assess, monitor, and strengthen bank-wide internal controls. Performs control testing, identifies design and operational gaps, supports RCSA and risk decisions, reduces duplication and manual effort, and explores systemisation and AI integration. The role also develops repeatable data-driven insights, normalises data, assesses variances, and communicates improvement opportunities to stakeholders.

Senior Consultant serving as a business-facing reinsurance data analyst in a consulting environment. The role analyzes reinsurance data structures, definitions, and flows; translates business needs into data requirements; validates data using SQL and Python; and supports data products, analytical datasets, and transformation initiatives. It requires strong reinsurance knowledge, stakeholder management, consulting skills, and collaboration with engineering and architecture teams.

Lead analytics for customer operations: build capacity planning and forecasting models, own data pipelines, develop predictive and causal inference analyses, track KPIs, standardise processes, and partner with operations, WFM, quality, and training to drive data-driven improvements.

Performs business-focused data analysis, SQL reporting, dashboard development, root cause investigations, data profiling, and proof-of-concept analytics. Partners with stakeholders, customers, BI, data engineering, and product teams to translate business needs into technical solutions. Uses Azure data platform technologies, Python, and Power BI to develop insights, improve reporting, support data governance, and influence data-driven decisions.

Own and maintain governed cost, schedule, benchmark, and project-performance datasets for global data center products. Build data models, validation routines, reconciliation processes, metadata structures, variance analyses, and Power BI dashboards. Partner with product, engineering, procurement, construction, developers, and IT teams to improve data quality, automate reporting, support techno-economic analysis, and provide trusted insights for product and infrastructure decisions.

Build and maintain investment data solutions, quantitative models, performance attribution systems, dashboards, and reporting workflows. Validate data quality, automate analytical processes, support backtesting and scenario analysis, apply model governance, and collaborate with investment, technology, and risk teams. The role also evaluates emerging technologies, including artificial intelligence and machine learning, to improve investment research and decision-making.

Build and maintain investment data solutions, quantitative models, performance attribution systems, dashboards, and automated reporting workflows. Partner with investment, technology, and risk teams to support portfolio construction, research, manager selection, and investment oversight. Responsibilities include validating data, improving data quality, applying model governance, conducting quantitative analysis, and evaluating emerging technologies such as artificial intelligence and machine learning.

The Staff Data Analyst leads complex projects, develops scalable data solutions, and mentors other analysts while driving strategic initiatives at Wise.

Conduct research and quantitative analysis across wholesale financial markets using large regulatory datasets, including transaction-level trading and derivatives data. Develop reproducible analytical code, identify market risks, evaluate regulatory outcomes, and produce evidence-based insights for policy and supervision teams. Collaborate with market experts and stakeholders, communicate complex findings clearly, and contribute to both ongoing research projects and time-sensitive market analysis.

Process, validate, and improve partner data using Excel, SQL, Azure Data Factory, Databricks, and Alteryx. Investigate data issues, support data projects, help develop AI-enabled workflows, and meet operational deadlines while maintaining accuracy and attention to detail. The role offers practical training and career growth within a collaborative data and technology team.

Leads business and data analysis for investment data products, translating stakeholder needs into governed, reusable data capabilities. Defines requirements, mappings, quality expectations, product roadmaps, and acceptance criteria; assesses data risks and value; collaborates with engineering, technology, and governance teams through delivery and testing. Supports Agile backlogs, OKRs, stakeholder discovery, and data product adoption while mentoring junior analysts.

Analyze transactional data to detect and prevent financial crime, manage AML alerts, build SQL-based models and dashboards, and improve transaction monitoring and customer risk-rating controls. The role owns financial crime operations capacity planning, models scenarios, identifies data-led improvements, and presents insights to business leaders while collaborating with Product, Compliance, and cross-functional stakeholders.

Analyze and reconcile claims data from internal systems, TPAs, bordereaux, and data warehouses. Build SQL extracts, dashboards, KPI reports, reserve reviews, severity analyses, and executive reporting. Support underwriting and actuarial decisions through claims insights, loss development analysis, data quality investigations, and portfolio trends. Help develop claims rules, scoring models, exception reporting, and UAT test cases using SQL, Python, Azure Databricks, and related data platforms.

Operate and enhance data security controls across enterprise, cloud, endpoint, network, collaboration, and AI environments. Monitor and investigate data loss, unauthorized access, insider threat, and policy violation alerts; tune DLP and classification tools; support incident response, governance, risk, and compliance activities; and collaborate with security engineering and architecture teams to improve detection, control effectiveness, and data protection processes.

Supports a front-office trading desk by maintaining, validating, and reconciling financial product data across internal databases and external APIs. Automates reporting workflows using Python and SQL, validates data published externally, identifies coverage gaps, and responds to ad hoc requests. Collaborates with trading, engineering, product, research, legal, risk, and distribution teams while developing knowledge of ETFs, ETPs, crypto markets, blockchain, and DeFi.

Supports FP&A transformation by building finance reporting frameworks, extracting and transforming ERP data, maintaining Alteryx automation workflows, and developing planning, budgeting, forecasting, and cash flow models. Partners with finance, IT, business intelligence, and wider business teams to improve data quality and create standardized commercial tools that support data-driven decisions.

Own GTM and sales analytics across pipeline performance, forecasting, funnel conversion, win/loss analysis, lead and account scoring, territory planning, quota modeling, and capacity planning. Partner with RevOps, Sales, Product, Finance, and Analytics Engineering to improve metric definitions, Salesforce and product data models, dashboards, and decision-making. Translate complex analysis into clear recommendations for GTM leadership.

Leads end-to-end data analytics projects supporting strategic commercial, operational, and financial decisions. Builds and governs Domo dashboards and reports, establishes data quality and governance standards, documents data lineage, and presents executive-ready insights. Partners with senior stakeholders across Product Operations, Commercial, Finance, and Leadership to prioritize analytics work, manage competing requests, and communicate trade-offs. Requires strong SQL, BI, data warehousing, ETL, stakeholder communication, and project ownership skills.

Collect, clean, validate, and analyze data using Excel, SQL, Power BI, and Microsoft Fabric. Create dashboards, reports, visualizations, KPI reporting, and actionable recommendations for business and client decision-making. Collaborate with analysts, consultants, and stakeholders, present findings clearly, maintain reporting solutions, and support data quality and project delivery. The role offers early-career development in data analytics and consulting.

Technical Business Analyst supporting a data-focused client project in Bristol. Responsibilities include gathering and documenting business and technical requirements, analyzing data, translating business needs into technical specifications, identifying data processes, facilitating stakeholder discussions, and supporting delivery teams throughout the project lifecycle. The role requires onsite work three days per week and is an Outside IR35 contract running until 18th December, with potential extension.

Produces regular people reporting, analyzes workforce data, responds to insight requests, performs data quality checks, and supports dashboards and data governance. The role translates HR data into clear insights for non-technical stakeholders, collaborates with HR Systems and People teams, and contributes to improved reporting processes and strategic workforce decisions.

Own marketing analytics across the inbound funnel, attribution, channel and campaign performance, CAC, LTV, and revenue contribution. Build dbt models and semantic-layer metrics using BigQuery and marketing platforms, maintain web and lead-data instrumentation, ensure data quality and GDPR compliance, and provide actionable insights to marketing, sales, and leadership.