Top Data Analyst Jobs in London

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 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.

Manage market data compliance activities, exchange and broker licensing, application onboarding, and external audits. Serve as a relationship contact for exchanges, brokers, internal stakeholders, and audit providers. Identify and mitigate compliance issues, communicate pricing and policy changes, and support cost efficiency, operational standards, and service improvements across proprietary applications.

Analyze scientific publishing and content acquisition data to guide product and business decisions. Design metrics, maintain analytics products and dashboards, conduct quantitative research, uncover trends and opportunities, and promote data literacy. The role requires advanced SQL and Python, data visualization, cloud data warehouse, and analytics engineering experience, including ETL pipelines, data modeling, and testing.

Manage Calero technology expense and market data inventory records, reconcile invoices, validate cost allocations, investigate variances, and support accounts payable. Administer market data licenses, exchange entitlements, vendor permission files, and joiner/leaver updates. Prepare reporting, support accruals and prepayments, audit processes, and coordinate with suppliers and internal stakeholders in a regulated environment.

The job description provides company background but does not specify responsibilities, qualifications, technologies, compensation, location, travel, or clearance requirements for the Digital Product Data Analyst role.

Analyze customer behavior and travel product performance using large datasets. Translate complex findings into actionable recommendations, dashboards, reports, and presentations for technical and non-technical stakeholders. Collaborate with Product, Design, Data Science, and external partners; help define success metrics and support evidence-based decision-making across travel and disruption initiatives.

Analyze participant support and biosample operations data to identify trends, bottlenecks, discrepancies, quality issues, and root causes. Develop reporting, metrics, monitoring, forecasts, and capacity analyses, using SQL and exploratory analysis across multiple systems. Partner with technical and operational teams to improve data quality, communicate findings, and provide recommendations that strengthen operational efficiency and participant experience.

The Data Analyst will develop analytical solutions, create Tableau or Power BI visualizations, query and wrangle complex datasets using SQL, troubleshoot data-process issues, identify root causes, and deliver actionable insights. The role includes gathering requirements, owning analytical projects through stakeholder presentation, supporting data teams, testing outputs, answering business questions, and collaborating with technical and non-technical stakeholders.

Partner with Product, Design, Engineering, and Analytics Engineering to analyze customer behaviour and product performance using funnel, cohort, and segmentation analyses; define and monitor KPIs; design and analyze A/B tests; build dashboards and reports; surface trends and recommendations; and apply AI/ML (including LLM-assisted) techniques to speed insight generation and measurement.

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, 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.

Supports, enhances, and optimizes business applications including Aderant Expert and Intapp Open. Gathers requirements, analyzes processes, coordinates testing, supports reporting and data quality, resolves application issues, maintains documentation, and assists with user training, change management, vendor coordination, and stakeholder engagement.

Analyze and improve fraud signals, user segmentation, and review processes for a player rewards platform. Investigate suspicious users, monitor segment performance, evaluate verification methods such as facial verification, ID scanning, and phone verification, and recommend third-party fraud tools. Partner with product and data teams to translate analysis into operational fraud controls as fraud patterns evolve.

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.

Develop and govern EMEA crisis and incident data landscapes, models, dashboards, and reporting. Produce management information, KPIs, KRIs, and resilience metrics for senior forums while ensuring data accuracy, auditability, and regulatory compliance. Support incident and crisis management teams, thematic reviews, maturity assessments, regulatory reporting, and integration with ServiceNow and critical event management platforms.

Build and support data pipelines, data assets, integrations, and models for enterprise analytics. Manage datasets for feature engineering, APIs, and ingestion; implement ETL across platforms; create reusable real-time and analytical data assets; and establish data quality monitoring and alerting. Collaborate with data scientists, data wranglers, and technical teams, with preferred hands-on GCP experience.

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.

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.

Evaluate and onboard cross-asset market data feeds, maintain and enrich reference databases, improve data QA, liaise with vendors, produce analytics and reporting, support product development and client requests across TCA, venue analytics and trade surveillance.

Own Dex’s data foundation as its first dedicated data hire. Design trusted data models, build pipelines, improve source instrumentation, create a self-service data platform, and structure data for analytical agents. Partner with product, growth, commercial, and operations teams to define meaningful metrics and answer complex business questions. Establish data quality, governance, and modeling standards while helping build the future data function.

Analyzes marketing, sales, customer experience, e-commerce, retail, and campaign performance data. Builds scalable BigQuery and Looker Studio dashboards, defines data models and commercial KPIs, measures campaign uplift and halo effects, analyzes customer journeys and content performance, and translates findings into actionable recommendations for cross-functional stakeholders.

Analyze marketing, sales, customer experience, e-commerce, retail, and promotional performance. Build and maintain scalable BigQuery-backed Looker Studio dashboards, define data models and commercial KPIs, measure campaign uplift and halo effects, analyze customer journeys and content performance, and translate findings into actionable recommendations for cross-functional stakeholders.

Develop Python-based geospatial analyses to quantify carbon removal and reductions for biosphere projects. Build above-ground biomass estimates, dynamic baselines, and uncertainty quantification approaches aligned with validation protocols. Support commercial analysis for prospective project developers, convert bespoke solutions into reusable code, and collaborate with Data Science and Biosphere Science teams. The role requires strong data analysis, geospatial data, cloud-hosted tools, pipeline management, machine learning integration, customer communication, and adaptability in a fast-paced in-person environment.