Senior Data Warehouse Analyst

Job Description:

• Own and evolve the structure of the analytics data layer (tables, views, aggregates)
• Ensure consistent definitions across key KPIs (GGR, NGR, deposits, bonus cost, player metrics)
• Eliminate duplication of logic and conflicting calculations across tables and reports
• Design and maintain scalable data structures to support analytics and reporting needs
• Work with backend/data teams to define how new data should be ingested and structured
• Ensure accuracy, completeness, and consistency of data across all reporting layers
• Identify discrepancies across datasets and trace issues back to source, transformation, or logic
• Design and implement structured QA and reconciliation processes
• Reduce recurring issues by improving underlying data structure rather than patching outputs
• Work with stored procedures and transformation logic to maintain and improve data pipelines
• Optimise queries, aggregations, and table design for performance and scalability
• Implement efficient data strategies (pre-aggregation, incremental logic, partitioning where applicable)
• Ensure data is performant and usable across BI tools and analytics workflows
• Act as the main point of contact for data issues between BI and backend/IT teams
• Translate business/data issues into clear technical requirements
• Drive resolution of issues end-to-end - from identification to validation
• Communicate clearly when data issues impact reporting or decision-making
• Document data structures, logic, and KPI definitions
• Create clarity around how data is built and used across the business
• Introduce repeatable processes to reduce manual work and firefighting
• Support the analytics team with SQL-based analysis when needed

Requirements:

• 5-8+ years in data, BI, or data warehouse roles
• Strong experience working with transactional datasets (e.g. bets, payments, events, financial data)
• Experience in iGaming or similar high-volume data environments preferred
• Experience working close to data modelling or warehouse layers (not just reporting)
• Advanced SQL (joins, aggregations, window functions, performance optimisation)
• Experience working with stored procedures and transformation logic
• Strong understanding of: data grain and aggregation layers KPI consistency and single source of truth principles designing reporting tables and datasets
• Experience improving data performance (large tables, optimisation, query efficiency)

Benefits:

• Health insurance
• Flexible work arrangements
• Professional development opportunities