Ajinkya Gaddime

• Developed and executed QA strategies for Microsoft Fabric pipelines and Lakehouse architectures, ensuring data accuracy,
completeness, and consistency across bronze, silver, and gold layers.
• Built and validated Fabric notebooks (PySpark / Spark SQL) to test data transformations, pipeline outputs, and migration
accuracy from legacy systems.
• Performed end-to-end data validation by reconciling source vs. target datasets using SQL queries and automated checks, ensuring
reliable financial reporting.
• Conducted Power BI report testing (UI + backend) by validating dashboard outputs, DAX measures, and semantic model
consistency with underlying Fabric data.

• Performed data validation and QA testing on 10-15M row datasets within Microsoft Fabric Lakehouse, ensuring high data
quality and reporting accuracy.
• Tested and validated Fabric data pipelines and transformations using SQL and Python, ensuring consistency between raw and
curated layers.
• Executed data migration validation from legacy systems to Microsoft Fabric, comparing outputs and ensuring functional
parity.
• Automated validation workflows within Fabric pipelines, improving data reliability and reducing manual QA effort.
• Built test cases and validation frameworks using Excel (pivot tables, lookups) to reconcile large datasets and identify
discrepancies.

• Designed and executed data quality checks and QA validation processes across reporting systems, improving
accuracy and consistency of business insights.
• Validated Power BI and Tableau dashboards by cross-checking backend SQL data, ensuring alignment between
reports and source systems.
• Built and maintained scalable data models across cloud platforms (AWS, GCP, Azure) and warehouses (Snowflake, Redshift),
integrating over 20M rows/day from multiple sources for cross-department analytics.
• Automated ETL/ELT pipelines using Python and the Great Expectations library for data validation, reducing manual effort and
improving processing efficiency by 25%.
• Ensured data quality and integrity through automated validation checks, auditing, and governance processes, achieving a 15-20%
improvement in accuracy across reports and dashboards.