Key skills
ETLSparkSQLAIELTAnalyticsBIDatabricksSQL Server
About this role
Role Overview
- Validate enterprise BI solutions aligned to Kimball dimensional modeling
- 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, Business rule validation, Regression testing, Data reconciliation
- Automate QA activities wherever possible to reduce manual effort and improve release velocity
- Validate Databricks pipelines and SQL transformations
- Perform QA of ETL/ELT patterns and validate SQL Server objects
- Execute performance testing and recommend strategies to improve platform performance
- Provide support for production execution and delivery of BI/analytics solutions
- Maintain accurate and complete QA documentation and ensure adherence to corporate policies
- Mentor team members on QA methods, automation, and data validation patterns
Requirements
- 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
Tech Stack
- ETL
- Spark
- SQL
Benefits
- N/A