Top Data Analyst Jobs in Belfast

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.

Leads Source to Pay data migration and supplier master data quality initiatives. Responsibilities include analyzing SAP and legacy data, profiling and cleansing supplier records, validating and reconciling migrations, resolving defects, strengthening governance, developing automation and quality frameworks, and monitoring data readiness. The role partners with procurement, finance, SAP, master data, and technical teams during enterprise transformation programs.

Analyze transactional data to detect and prevent financial crime, manage AML alerts, build SQL-based models and dashboards, and improve transaction monitoring and customer risk-rating controls. The role owns financial crime operations capacity planning, models scenarios, identifies data-led improvements, and presents insights to business leaders while collaborating with Product, Compliance, and cross-functional stakeholders.

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.

Analyze complex capital and related data for financial markets, delivering insights, visualizations, and recommendations to product desks and business partners. Identify trends, optimization opportunities, data-quality issues, and process improvements across multiple platforms. Collaborate with stakeholders and Technology to resolve complex requirements and systems issues, while communicating findings clearly and supporting capital-management initiatives.

Develop and maintain enterprise Power BI and Tableau reporting solutions, data pipelines, models, dashboards, and analytics using Snowflake, SQL, dbt, and Alteryx. Analyze institutional and external data to support strategic planning, forecasting, recruitment targets, and executive decision-making. Ensure data quality, reporting governance, release standards, and stakeholder satisfaction. Provide technical guidance, training, and support across the University, contribute to emerging AI-enabled analytics, and potentially line-manage junior developers.

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.

Build and enhance dashboards and counterparty credit risk exposure tools for crisis management, innovation, and regulatory initiatives. Analyze counterparty credit risk and XVA Trading issues, collaborate with trading, risk, operations, and technology stakeholders, and manage time-sensitive projects. Identify opportunities to incorporate AI-driven innovations into analytical tools and processes. Requires advanced SQL and Tableau, strong quantitative and analytical skills, and ideally derivatives modeling knowledge.

Analyze and manage reference data products for a global corporate actions platform. Define business and functional requirements, data models, mappings, user stories, and product backlogs. Partner with technology, operations, vendors, and stakeholders to develop interfaces, validate data, publish information downstream, and improve governance, automation, and data quality. Perform SQL-based analysis, documentation, user training, process streamlining, and support AI-ready data initiatives.

Lead measurement, reporting, and optimization of global marketing performance. Build dashboards and reporting frameworks across GA4, Salesforce, Pardot, paid media, and social. Analyze campaign, funnel, pipeline, and ROI performance, establish KPI definitions and governance, and provide actionable insights to marketing, sales, and executive stakeholders.

Analyze and integrate multiple data sources, build conformed data models, and create accessible Power BI dashboards and reports. Apply statistical techniques including regression, correlation, and significance testing to generate actionable insights. Clean and transform data using SQL, Python, Power Query, and M, while communicating findings to technical and non-technical stakeholders and recommending emerging visualization technologies.

Owns the integration and analysis of people, security, technology, fraud, physical security, investigation, and governance data to identify insider-risk patterns. The role produces actionable intelligence, supports investigations, strengthens controls, and develops monitoring, automation, governance, and reporting capabilities. It requires influencing senior stakeholders, translating complex findings into business outcomes, and making risk-based recommendations amid ambiguity.

Delivers data governance initiatives by gathering requirements, documenting processes and data flows, supporting Microsoft Purview adoption, and improving data quality. Works with data owners, stewards, technical teams, and senior stakeholders to define governance processes, metadata standards, business glossaries, ownership models, controls, and quality metrics. Facilitates workshops, translates business needs into requirements and user stories, investigates data issues using SQL and profiling outputs, and communicates risks, dependencies, and decisions.

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.

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.

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.

Own and develop the company’s Power BI reporting environment, including semantic models, dashboards, advanced DAX, Power Query transformations, governance, deployment, and performance optimization. Collaborate with Data Engineering and business stakeholders to define KPIs, validate data, improve reporting quality, and deliver scalable BI solutions. Support data architecture, data quality, ad hoc analysis, and continuous improvement across enterprise reporting assets.

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.

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.

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.