Top Data Analyst Jobs in London

Own analytics across the participant lifecycle to support acquisition, onboarding, activation, engagement, quality, retention and reactivation. Build metrics, dashboards, and experimentation frameworks with SQL and Python; partner with Product, Operations, Growth, and Data Science to run experiments and translate marketplace data into actionable decisions that improve participant health and supply sustainability.

Analyze and model electricity grids across global markets, including power flows, grid constraints, interconnection queues, stability, and techno-economic optimization. Support grid investment plans, capex strategy, and non-wire solutions such as flexibility and battery energy storage. Develop data and modeling tools, generate insights from complex energy datasets, and communicate findings clearly to technical and non-technical stakeholders.

Owns content analytics end to end, including engagement measurement, event instrumentation, audience analysis, content classification, competitive coverage tracking, and editorial attribution. Builds dbt models in BigQuery or similar warehouses and maintains the Omni semantic layer for trusted self-service reporting. Partners with editors to diagnose performance drivers, prioritize coverage, and translate complex analysis into actionable editorial strategy.

Leads end-to-end business analysis for AI, automation, and data initiatives within the tax technology function. Responsibilities include stakeholder workshops, requirements gathering, process analysis, user stories, acceptance criteria, backlog refinement, traceability, dependency and risk management, solution alignment, and UAT planning and execution. The role bridges tax business stakeholders with product, architecture, delivery, and testing teams to ensure technology solutions meet agreed business needs.

Work cross-functionally to design and execute data-driven strategies across pricing, promotions, forecasting, growth, credit risk, and product analytics. Build models and monitoring, evaluate new product/channel opportunities, present insights to leadership, validate deployed strategies, and mentor junior analysts to scale the business.

Lead analysis of operational data to support decision-making. Build dashboards and reports, partner with operations stakeholders to identify process improvements, monitor KPIs, and inform strategic initiatives.

As a Senior Data Analyst, you will gather and document business requirements, manage stakeholders, analyze complex datasets, and create data models and dashboards.

As Lead Data Analyst, you will monitor and ensure the accuracy of forecasting models, diagnose forecasting errors, and communicate findings to stakeholders for operational improvement.

Partner with Product and Engineering to define metrics, run root-cause analyses and experiments, build tracking, reporting, and automated alerts, model data in dbt/BigQuery, scale AI-assisted analytics for self-serve, and communicate insights to drive product and business decisions.

Supports data and digital capability design, delivery, governance, and adoption across insurance operations. Translates stakeholder needs into requirements, user stories, process models, solution options, and prioritized backlogs. Partners with platform and delivery teams to assess feasibility, manage dependencies, oversee implementation, and ensure alignment with architecture, controls, and business objectives. Monitors product adoption and effectiveness while improving data literacy and business readiness.

Translate business and regulatory requirements into reference data models, datasets, data quality rules, workflows, and documentation. Partner with architects and stakeholders across Trading, Compliance, Operations, and Research. Serve as a subject matter expert on equity and ETF reference data, corporate actions, identifiers, lifecycle processing, and downstream impacts. Support data interrogation using SQL and contribute to system delivery, data dictionaries, entity relationship diagrams, requirements specifications, and exception-management metrics.

Lead CRM analytics for Personal and Business segments: design measurement frameworks, run campaign and lifecycle analyses, apply A/B and incrementality testing, build segmentation models, maintain dashboards, ensure data quality, and partner with CRM, Product, and Data teams to optimise customer engagement and retention.

Join the Integration team to collect, validate, map and load high-volume financial data from clients into SteelEye's analytics platform. Engage stakeholders, resolve data queries, refine operational processes, and perform industry research to meet data quality metrics.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Supports enterprise data governance and quality by mapping data flows, documenting processing activities, maintaining business glossaries, defining quality rules, assessing data controls, reporting weaknesses, and producing stakeholder and regulatory reports. The role partners with business and IT leaders, data owners, stewards, and subject-matter experts to promote compliance with AIG data policies, GDPR, and other privacy requirements.

Collect, clean, and transform large datasets; build and maintain data models and reports (using Snowflake and BI tools); perform statistical and machine-learning analyses; define KPIs; ensure data governance and accuracy; present insights to stakeholders; collaborate with data engineers and follow software development best practices.

Own and build the support reporting layer: ticket volume, SLA, resolution times, CSAT/CES/sentiment, and capacity. Produce dashboards and analyses for leadership and engineering, maintain data quality and governance, prioritize issues using multi-signal models, and surface Voice of Customer trends to close the loop with Product and GTM.

Serve as a senior analytics partner to underwriting and related teams, translating commercial questions into analyses, dashboards, and actionable insights. Deliver complex analytics workstreams, govern Power BI development and releases, apply SQL and data modeling standards, and support automation and AI-enabled workflows. Coach analysts, conduct peer reviews, collaborate with Data Engineering, and contribute to Agile planning, stakeholder engagement, and external data propositions.

Join a hedge fund Compliance Control Room to manage restriction lists, monitor position limits, support MNPI and information barrier controls, field business queries, document and refine controls, gather requirements, and lead data and process improvement initiatives alongside technology teams.

The FP&A Analyst will translate financial data into management information, support budgeting, improve forecasting, and collaborate across departments to enhance the operating model.

Lead investigation and resolution of broker margin data discrepancies by validating Python pipeline outputs against internal sources. Debug raw broker files, document margin calculation logic across account types, work with Data Engineering to translate business rules into Python, escalate structural data gaps, liaise with brokers and SMEs, and maintain mapping and audit documentation.

Partner with founders and cross-functional teams to analyze financial and operational data, build executive dashboards, create P&L and margin analyses, forecast and model unit economics, identify cost leaks, reconcile transactions, and present recommendations to improve profitability, cash flow, and business performance.

The Senior Data Analyst will enhance courier performance management and optimize handovers by modeling trade-offs, running experiments, and improving data analytics.

The Forward-Deployed Data Analyst will engage with African Ministries of Health, build data management solutions, develop dashboards, and support public health decision-making.

Serve as product-owner/business analyst for actuarial data and tools within the Global Data Office. Translate actuarial requirements into data engineering specs, lead Agile ceremonies, manage product backlog and roadmaps, align stakeholders, and ensure data governance, lineage, testing, and quality for actuarial reporting, reserving, and pricing use cases.