Top Data Analyst Jobs in Edinburgh

Maintain and transform investment data by building data systems and pipelines, performing complex analysis, combining multiple sources, improving data quality, and presenting results via visualization. Collaborate with clients, system architects, and product teams to deliver actionable insights and analytical tools for data-driven decisions.

Perform data collection, analysis, and interpretation to identify trends. Build and maintain dashboards and reports, ensure data accuracy through quality checks, collaborate with teams to deliver actionable insights, and support development of analysis methodologies.

Lead the data solutions strategy for PR&C PSP division, managing data initiatives, teams, and collaborating on analytics projects. Ensure compliance, governance, and optimize data services while communicating technical solutions to stakeholders.

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

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.

The Market Data Analyst will oversee data quality and operations for metals and minerals, monitor market trends, support clients, and improve data workflows using SQL and Python.

The Platform Data Analyst will enhance visibility and performance optimization of Runware's media AI platform through analytics, monitoring, and reporting. Responsibilities include tracking performance, building dashboards, and collaborating with engineering and DevOps teams to improve observability and reliability.

Perform full-stack on-chain and product analytics: query on-chain data, profile ecosystem actors, benchmark protocols, define KPIs, build dashboards, and translate findings into actionable briefs for strategy, BD, product, and growth teams.

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Lead end-to-end SAP data migration for large S/4HANA programs: analyze legacy and target models, define mappings, perform profiling and cleansing, build migration datasets with SAP tools, develop reusable frameworks and automation, create data quality dashboards, support cutover and post-go-live stabilization, and mentor data teams.

Analyze large datasets to identify trends and deliver actionable insights; design and monitor KPIs; build and maintain dashboards and reporting frameworks; conduct ad hoc analyses; ensure data quality and support reporting infrastructure in partnership with product and business teams.

Implement, audit and troubleshoot GA4 and Google Tag Manager setups, manage conversion tracking across major ad platforms, maintain server-side tracking, diagnose data quality issues, and build client-facing reports and dashboards. Handle ad-hoc tickets and client-facing delivery across DTC and B2B eCommerce accounts.

Lead analytics strategy and partner with Finance, Sales, Marketing, and Product to design, automate, and maintain metrics, dashboards, and analyses. Ensure data quality (dbt), build scalable analytics solutions, embed AI-driven and agentic workflows, and translate insights into actionable business outcomes to improve acquisition, retention, and growth.

Partner with product and business teams to define metrics, design and analyze experiments, build analytical frameworks, maintain governed dashboards, conduct deep-dive analyses (funnels, retention, cohorts), size opportunities, and connect product metrics to revenue to inform roadmap and leadership decisions.

Support implementation of data governance frameworks across Pensions & Savings. Work with Data Owners, Stewards, and Risk to monitor data quality, track governance actions, maintain documentation, and produce regular and ad-hoc reports to support regulatory compliance and governance maturity. Contribute to continuous improvement of data governance processes and stakeholder reporting.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

The Data Product Analyst designs and manages data products for insurance domains, ensuring they meet analytical and compliance standards while collaborating with cross-functional teams.

Lead high-complexity analytics projects: partner with stakeholders, prepare and blend large datasets, apply intermediate statistics, build models and visualizations, deliver insights, and mentor junior analysts.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

Work with stakeholders, product and technical teams to elicit, document, and translate business and data requirements into user stories and acceptance criteria. Support backlog refinement, run workshops, assist data analysis and validation, and identify process improvements to enable data-driven decisions.

Use SQL and Python to analyse transaction monitoring and fraud rule performance, identify patterns and anomalies, reduce false positives, and prioritise high-risk cases. Partner with FinCrime, Product, Compliance and Data teams to improve controls, data pipelines and detection logic, support scoring and future ML use cases, and translate analysis into actionable recommendations that balance fraud prevention with customer friction.

The Director Analyst focuses on data integration and engineering, providing insights, advice, and research to help organizations improve their data practices and strategies.