The Senior Data Analyst is a highly specialized role responsible for designing, developing, and governing advanced analytics solutions that transform complex operational data into reliable insights and scalable decision-support products. The role combines strong business acumen with deep technical expertise in data modelling, SQL, Power BI, data integration, automation, and analytical quality assurance.

This position owns the end-to-end analytical lifecycle, from requirements discovery and source assessment through data preparation, semantic modelling, visualization, deployment, monitoring, documentation, and continuous improvement. It partners with Operations, Finance, HR, Talent, Technology, and other stakeholders to define KPIs, improve data reliability, standardize reporting, and enable data-driven decisions.

The role also provides technical guidance to analysts, establishes development standards, and contributes to the evolution of the global data and BI capability.

1. Advanced Analytics & Business Insight
• Translate complex business questions into analytical frameworks, measurable KPIs, and actionable insights.
• Perform exploratory, diagnostic, trend, variance, segmentation, and root-cause analysis.
• Build reusable analytical datasets and identify patterns, risks, anomalies, and performance opportunities.
• Present findings through concise executive narratives, visualizations, and evidence-based recommendations.

2. Data Modelling & Business Intelligence
• Design, develop, and optimize Power BI data models, dashboards, and reporting products.
• Build semantic models using dimensional modelling, star schemas, relationships, calculation groups, and reusable measures.
• Develop advanced DAX measures and Power Query transformations focused on accuracy, performance, and maintainability.
• Apply row-level security, refresh strategies, workspace governance, and deployment standards.
• Monitor report performance, troubleshoot bottlenecks, and improve user experience.

3. SQL, Data Integration & Automation
• Write advanced SQL using joins, CTEs, window functions, aggregations, subqueries, and performance-oriented techniques.
• Integrate, cleanse, transform, and reconcile data from multiple structured and semi-structured sources.
• Design automated data-preparation and reporting workflows using Power Query, Power Automate, APIs, SharePoint, and approved enterprise tools.
• Partner with engineering and system owners on source availability, data contracts, integration logic, and issue resolution.

4. Data Quality, Governance & Documentation
• Define validation rules, reconciliation controls, data-quality checks, and exception-management processes.
• Investigate inconsistencies and coordinate corrective actions with source owners.
• Maintain data dictionaries, KPI definitions, lineage, transformation logic, technical documentation, and release notes.
• Support access controls, privacy, retention, and responsible data-use requirements.

5. Stakeholder Partnership & Technical Leadership
• Lead requirements sessions and convert business needs into scalable analytical specifications.
• Manage analytical priorities, dependencies, delivery risks, and stakeholder expectations.
• Review analytical outputs and provide technical guidance, peer review, and mentoring.
• Promote reusable components, version control, and disciplined development practices.