Top Data Analyst Jobs in Edinburgh

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.

Monitors and maintains client asset and securities data, validates accuracy against market sources, evaluates data vendors, and supports reference data products. The role responds to data inquiries, documents workflows, performs basic SQL analysis, maintains real-time investment data, ensures risk and control compliance, and analyzes information flows between accounting and custody systems. It also develops expertise in fixed income, equities, corporate bonds, and related investment processes.

The Data Analyst will analyse complex datasets, develop dashboards and reports, translate stakeholder requirements into data-driven solutions, and support analytical models and AI-enabled capabilities. The role involves collaborating with engineers, architects, and business analysts; ensuring data quality and governance; participating in Agile delivery; presenting insights; and supporting UX/UI for digital tooling within a secure Defence programme.

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

Conduct research and quantitative analysis across wholesale financial markets using large regulatory datasets, including transaction-level trading and derivatives data. Develop reproducible analytical code, identify market risks, evaluate regulatory outcomes, and produce evidence-based insights for policy and supervision teams. Collaborate with market experts and stakeholders, communicate complex findings clearly, and contribute to both ongoing research projects and time-sensitive market analysis.

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.

Own GTM and sales analytics across pipeline performance, forecasting, funnel conversion, win/loss analysis, lead and account scoring, territory planning, quota modeling, and capacity planning. Partner with RevOps, Sales, Product, Finance, and Analytics Engineering to improve metric definitions, Salesforce and product data models, dashboards, and decision-making. Translate complex analysis into clear recommendations for GTM leadership.

Collect, clean, validate, and analyze data using Excel, SQL, Power BI, and Microsoft Fabric. Create dashboards, reports, visualizations, KPI reporting, and actionable recommendations for business and client decision-making. Collaborate with analysts, consultants, and stakeholders, present findings clearly, maintain reporting solutions, and support data quality and project delivery. The role offers early-career development in data analytics and consulting.

Produces regular people reporting, analyzes workforce data, responds to insight requests, performs data quality checks, and supports dashboards and data governance. The role translates HR data into clear insights for non-technical stakeholders, collaborates with HR Systems and People teams, and contributes to improved reporting processes and strategic workforce decisions.

Leads research and advisory work for CIOs on data, analytics, and AI strategy, governance, operating models, architecture, value realization, and emerging technologies. Develops thought leadership, market analysis, presentations, and actionable client guidance; evaluates agentic and composite AI, modern data platforms, AI-ready data, organizational maturity, and capability-building needs. Represents Gartner at client meetings and conferences, supports sales, mentors colleagues, and shapes research agendas.

Embedded within the commercial team, the Staff Data Analyst will own trusted metrics for conversion, contracted volume, sales, margins, pipeline, and forecasting. Responsibilities include improving forecast accuracy, diagnosing CRM and transactional data quality issues, analyzing commercial opportunities and partner performance, identifying risks and trends, and translating complex findings into recommendations for senior stakeholders. The role also promotes metric adoption and self-service analytics across non-technical commercial teams.

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.

Investigate and improve the quality of chartering datasets covering vessels, ownership, fixtures, cargo orders, positions, and shipping zones. Use SQL and Python to analyze large datasets, identify inconsistencies, develop validation checks, and investigate root causes. Review AI-parsed fixture data, maintain geographic mappings in QGIS, establish data-quality metrics, and collaborate with Product, Data, Research, Engineering, and AI/ML teams to drive systematic improvements.

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.

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.

Embedded within the commercial team, the Senior Data Analyst will own core sales and revenue metrics, improve pipeline and forecast accuracy, investigate CRM data quality, size commercial opportunities, and analyze partner, retention, and renewal performance. The role translates ambiguous business questions into actionable recommendations, defines trusted metric frameworks, communicates insights to senior stakeholders, and increases self-service analytics adoption across non-technical commercial teams.

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.

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.

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

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.

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.

Lead marketing measurement: own attribution, incrementality testing, and media mix modeling. Design and analyze experiments, build repeatable templates and self-serve analytics, translate results into budget and growth recommendations, ensure data integrity, and embed AI tools into analysis workflows to measure pipeline and revenue impact.