Top Data Analyst Jobs in London

Analyze, clean, validate, and reconcile data from multiple sources; write SQL; maintain databases; build dashboards, reports, and KPIs; investigate discrepancies and integration failures; support migrations and data pipelines; and provide UK/Europe clients with data-related support. The role also requires documenting incidents, explaining technical findings to non-technical stakeholders, monitoring processing jobs, and collaborating with Product, Engineering, and Program Management.

Analyze paid social audiences and platform performance to identify bidding, budget, and testing opportunities. Design experiments, validate measurement signals, maintain dbt data pipelines, and build reliable BI dashboards. Develop analytical frameworks for budget optimization, investigate anomalies, and communicate insights, risks, and trade-offs to marketing stakeholders. Collaborate with platform, data science, marketing science, and analytics engineering teams while applying attribution, unit economics, incrementality, and marketing measurement expertise.

Lead pricing analytics for Wise Platform by investigating cost data, improving data quality and allocation methodologies, monitoring financial performance, and identifying profitability and growth opportunities. The role requires building complex SQL data sources, developing reusable dbt models, explaining product economics, influencing pricing strategies, and partnering with analytics, engineering, and business teams.

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.

Lead analytics for FinCrime operations: own forecasting and capacity planning, maintain data pipelines, build predictive and causal models, assess operational health and costs, implement KPI frameworks, track initiative performance, and standardise processes across forecasting, scheduling, and real-time operations.

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.

Own data extraction and integration across warehouses, business systems, and APIs. Build decision-focused Power BI or Tableau dashboards, answer urgent business questions, automate manual reporting with Power Automate or Zapier, and provide meaningful data insights to Finance, Sales, Operations, and Marketing. The role offers greenfield ownership and requires strong SQL, analytical ability, and independent project management.

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.

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

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.

Conduct exploratory data analysis to improve financial crime controls and transaction monitoring rules. Tune thresholds to balance detection accuracy with operational workload, support partner risk monitoring, and maintain financial crime key risk indicators and reporting. Perform deep dives into emerging risks, identify compliance gaps, and recommend measures to mitigate suspicious activity across Wise Platform. Collaborate with Product, Compliance, Fincrime, KYC, and Business Operations teams.

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.

The Senior Data Analyst will design and analyze A/B tests, apply statistical methods, build Power BI semantic models and dashboards, and collaborate with data engineering teams on Databricks pipelines. The role includes data governance, quality monitoring, performance optimization, stakeholder presentations, and advising client teams. It is a client-facing hybrid position requiring three to four days weekly onsite in Central London.

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.

Deliver customer and CRM analytics for Avios Retail: build segmentation and audiences, analyse campaign performance across Email/SMS/Push, design A/B tests, partner with Product, Trading, Engineering and Data to capture and activate customer data, and create reporting frameworks to drive personalised marketing and commercial decisions.

Manages ESG investment data operations globally, ensuring accuracy, timeliness, quality, controls, and regulatory readiness. Investigates data issues, resolves exceptions, partners with stakeholders and external suppliers, maintains governance documentation, monitors operational risk, and supports audits and technology change initiatives. Uses SQL, Python, and Excel to analyze datasets, automate processes, strengthen controls, and improve ESG data services.

Generate actionable insights from complex customer, behavioral, lifecycle, and product data. Collaborate with product, commercial, CRM, content, and leadership teams to define analytical questions, test hypotheses, measure business impact, and support evidence-based decisions. Present clear data stories, challenge assumptions, promote data-driven decision-making, and automate recurring reporting and analysis.

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.

Analyze Wise Platform pricing, costs, and profitability to identify pricing improvements and growth opportunities. Investigate complex cost data, improve data quality and allocation methodologies, explain product economics, monitor financial performance, and support pricing model enhancements. The role requires advanced SQL, visualization, dbt data modeling, stakeholder influence, and collaboration across analytics, engineering, and business teams.

Generate actionable growth and financial insights through dataset curation, metric development, statistical modeling, and deep-dive analysis. Analyze company and regional growth drivers, customer segments and cohorts, and the financial impact of marketing and product initiatives. Build data visualizations and communicate findings to influence business decisions, while managing stakeholders and organizing work independently.

Own end-to-end regulatory reporting datasets across multiple jurisdictions, including regulatory data mapping, automated pipelines, visualizations, data quality controls, monitoring, and discrepancy resolution. Collaborate with finance, compliance, regulatory, product, and analytics teams to support licensing, audits, partnerships, and controlled growth. Use SQL, dbt, Snowflake, Airflow, visualization tools, and AI capabilities to deliver accurate, scalable reporting infrastructure.

Analyze and interpret complex datasets to support business decisions; perform data validation, profiling, reconciliation and root-cause analysis; develop SQL queries and Power BI reports; present insights to stakeholders; support data onboarding, automation/AI initiatives and process improvements to enhance data quality and operational efficiency.

Visualise and analyse maintenance and operational performance across water supply assets and processes. Develop Power BI reports, performance measures, risk visualisations, and automated reporting to identify trends and support mitigation actions. Collaborate with subject matter experts and stakeholders, ensure data ownership and quality, promote cybersecurity and GDPR compliance, and provide insights that drive operational improvements across abstraction, treatment, transmission, pumping, and storage activities.

Monitor product functionality and financial crime data, delivering SQL-based analysis, extracts, dashboards, and operational controls. Investigate issues, support customer operations, monitor internal and third-party functionality, complete recurring processes, and automate management information using SQL and Python. The role collaborates with Operations, Compliance, and product teams in a fast-growing fintech environment.

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.