- Validate enterprise BI solutions aligned to Kimball dimensional modeling (facts, dimensions, conformed dimensions, SCD handling).
- Work with business teams to confirm business rules, acceptance criteria, KPIs, and reconciliation logic for BI/analytics outputs.
- Perform end to end QA across ingestion → transformation → semantic layer → reporting/consumption.
- Analyze TPA client files and record variances.
- Define QA standards, guidelines, and best practices for analytics engineering and reporting teams.
- Use AI tools to build test cases, test data and testing plans wherever applicable.
- Create and maintain test frameworks for:- Data correctness (row counts, aggregates, null handling, duplicates)
- Business rule validation (KPI logic, exclusions, thresholds)
- Regression testing (pipeline and reporting changes)
- Data reconciliation (source to target, cross system checks)
- Automate QA activities wherever possible to reduce manual effort and improve release velocity.
- Validate Databricks pipelines (Delta tables, notebooks, jobs, workflows) and SQL transformations (Spark SQL / T-SQL).
- Perform QA of ETL/ELT patterns including incremental loads, CDC patterns, and partitioning strategies.
- Validate SQL Server objects such as stored procedures, views, tables, indexing strategies, and job schedules where applicable.
- Execute performance testing for Databricks jobs and SQL Server queries (query tuning, indexing suggestions, cluster sizing guidance).
- Recommend strategies to improve platform performance, cost efficiency, and workload stability.
- Provide support for production execution and delivery of BI/analytics solutions.
- Support platform upgrades and migrations (Databricks runtime upgrades, cluster policies, SQL Server version changes) in dev/test, including QA sign off and documentation.
- Maintain accurate and complete QA documentation: test plans, test evidence, defect logs, reconciliation results.
- Ensure adherence to corporate policies, governance, and established practices.
- Mentor team members on QA methods, automation, and data validation patterns.