Top Data Analyst Jobs in London

Lead growth & adoption analytics by building and maintaining agent-ready curated datasets, partnering with engineers and product teams, reviewing merge requests, defining pipelines and metrics, performing advanced SQL analyses on petabyte-scale data, and communicating actionable insights to stakeholders.

Lead data analytics for Treasury Operations by analyzing performance, automating processes, building dbt data models, and maintaining Lightdash dashboards. The role supports corporate treasury and banking-partner decisions, translates business needs into analytical solutions, collaborates across finance and engineering, and mentors analysts while ensuring accurate, reproducible work.

Support investment teams by acquiring, cleaning, and analyzing multi-source data to test investment theses, identify inflection points, and present research. Learn fundamental investor approaches, build decision-support tools, and communicate findings clearly to Sector Data Analysts and investment teams.

Supports Finance and Go-To-Market departments by designing data recording systems, monitoring data quality, enforcing governance, analyzing large datasets, producing concise reports, and recommending process improvements. The role translates complex findings into actionable insights for non-technical stakeholders and supports Sales, Marketing, and Customer Success objectives.

Own the requirement-to-reporting lifecycle for compliance analytics, translating business needs into functional specifications, metrics, data mappings, transformation logic, and reporting products. Partner with business leaders, data engineers, and executives to address data gaps, define KPIs, build scalable pipelines, conduct UAT, validate data integrity, and deliver intuitive dashboards using Tableau, Power BI, Mode, or similar tools.

Analyze large-scale security telemetry, threat intelligence, endpoint, and detection data to identify trends, anomalies, and emerging threats. Build dashboards, reports, data pipelines, and KPI frameworks using SQL, Python, LogScale, or similar tools. Collaborate with data engineering, product, and engineering teams to ensure data quality and translate complex findings into actionable insights for technical and non-technical stakeholders.

Own and improve Wise’s marketing data activation ecosystem, including Reverse ETL pipelines, SQL/dbt models, monitoring, observability, identity resolution, and advertising-platform integrations. Diagnose end-to-end data flow issues and collaborate with analytics, data, engineering, and paid media teams to deliver reliable customer audiences and conversion signals.

Analyze user segments, targeting strategies, and A/B tests to generate recommendations that improve user journeys and business outcomes. Build monitoring and reporting dashboards using SQL, Databricks, and Domo, lead analytics projects, and apply AI to automate analytics processes. Communicate complex findings to technical and non-technical stakeholders while collaborating across the Targeting team.

Supports the BI team by performing data integrity checks, investigating reporting issues, managing small-scale changes, testing releases, administering reporting security, and helping develop Power BI dashboards. The apprentice will collaborate with business and development teams to improve self-service reporting, automate workflows, and progressively gain hands-on experience with SQL and Power BI while completing a Level 4 Data Analyst Apprenticeship.

Analyze Sportsbook product performance through testing, reporting, ad hoc queries, and data-driven insights. Partner with product stakeholders, Analysts, and Data Scientists to optimize user experiences, track metrics, solve business problems, and support product development. Present findings to senior stakeholders and serve as a trusted source of analytical intelligence.

Administers Orchard’s HRIS, maintains employee data integrity, manages system configuration and access, resolves issues, supports upgrades and integrations, and develops HR reports and dashboards. Analyzes workforce data, prepares management information, supports benefits and HR operations, and contributes to HR systems, process improvement, and AI adoption initiatives.

Supports HR data migration and system implementation activities, including data cleansing, mapping, validation, reconciliation, testing, reporting, and quality assurance. The role works with HR stakeholders and business users to resolve data issues, validate migration outcomes, maintain quality documentation, support user acceptance testing, and report on data readiness, workforce data accuracy, adoption, and system utilization.

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.

Manage, transform, cleanse, validate, and analyze complex datasets supporting employee listening and research programs. Develop automated data workflows and reporting processes, deliver accurate insights, and collaborate with consultants, researchers, clients, and internal stakeholders. The role also involves explaining technical concepts, maintaining data quality, meeting deadlines, and using AI tools to improve analytical efficiency.

Own and improve quantitative financial datasets supporting research and investment workflows. Design schemas, databases, pipelines, and DAGs; extend historical and high-frequency coverage; onboard and reconcile vendor data; and build automated validation, monitoring, dashboards, and investigation tools. Diagnose anomalies, resolve root causes, document standards, and collaborate with quantitative researchers, portfolio managers, vendors, and distributed global teams. The role requires substantial buy-side investment data experience, strong Python and SQL, and expertise in financial data quality.

Lead analytics for Wise Business Sales and Total Service teams by building dashboards, forecasts, cohort reports, and performance analytics. Optimize sales and service funnels, portfolios, lead management, conversion, churn prevention, SLAs, and team capacity. Conduct market sizing and expansion analysis, support go-to-market strategy, and establish reliable data foundations with Analytics Engineering. Translate commercial data into actionable recommendations while managing cross-functional projects and stakeholders.

Analyze operations demand drivers, build KPI and funnel frameworks, identify cost and efficiency opportunities, and deliver commercially grounded recommendations to senior leadership. Develop Python- and LLM-based automation prototypes, test them with operations data, and partner with product managers and engineers to scale successful solutions into production workflows.

Analyze and maintain corporate datasets, develop reports and automated dashboards, advise on data quality, support project and manpower-recharge systems, model business scenarios, and provide data expertise to clients and senior management. The role also involves tracking project critical paths, preparing client-pitch materials, and documenting requirements throughout analytics and reporting projects.

Leads digital data and analytics roadmap delivery, data governance, taxonomy, measurement frameworks, dashboards, experimentation, and AI-enabled analytics. Partners with marketing, technology, and client experience stakeholders to translate business needs into trusted reporting, reusable data products, actionable insights, and Adobe Experience Platform requirements.

Own analytics across the participant lifecycle, including acquisition, onboarding, activation, engagement, retention, churn, and reactivation. Build metrics, dashboards, reporting frameworks, and experimentation analyses using SQL and Python. Partner with Product, Operations, Growth, Data Science, Commercial, and Finance to identify marketplace opportunities, evaluate experiments, and translate participant and supply data into actionable commercial and strategic decisions.

Analyze complex education-related datasets to identify trends, risks, and opportunities; develop dashboards, reports, and visualizations; and translate findings into actionable recommendations. The role supports education quality assurance, regulatory monitoring, performance reporting, data governance, and evidence-based decision-making. It involves integrating internal and external data, analyzing Microsoft Dynamics 365 information, improving reporting methods, and collaborating with stakeholders across education, technology, and regulatory teams.

Supports the Chief Data and Analytics Officer by identifying operational risks, designing and evaluating controls, managing control issues and remediation, coordinating audits, supporting regulatory engagements, and overseeing governance and reporting. The role presents updates to senior management, collaborates with control managers, and helps establish best practices for data, AI/ML, and finance control environments.

Lead Data Analyst responsible for advanced analytics, quantitative analysis, experimentation, and commercial modeling. Partner with Data Engineering to implement scalable pipelines, drive insights into decision-making, and embed customer research into operations. Mentor analysts, build data literacy, and influence cross-functional strategic and tactical decisions.

Build and enhance AWS QuickSight reporting for Customer Service, create reusable datasets, integrate business data sources, and establish consistent metrics and documentation. The role translates stakeholder needs into dashboards, automates manual reporting, validates data quality, investigates issues, and supports self-service analytics and knowledge transfer during a six-month engagement.

Analyzes, cleanses, validates, and enhances customer risk and control data within the CUBE Network. Supports data matching using Python-based tools, collaborates with subject matter experts, and works with Product and Engineering to improve processing workflows. Helps define risk and control indices, automates analysis, and maintains high standards of data quality, consistency, and accuracy.