Pradhuman Singh Parmar

ETL and Data Engineering professional with 4+ years of experience specializing in building scalable ETL pipelines and optimizing data workflows using Informatica PowerCenter, IICS, Databricks, and modern ETL frameworks. Proficient in data warehousing, integration, transformation, and cloud-based data pipelines with a strong focus on data quality, performance tuning, and reliable data processing.

Data Engineering Sr Analyst at Accenture (2025-11 – Present)

Databricks Migration Project

• Spearheaded migration from Hive to Databricks, enabling scalable and modern data processing
• Built a metadata-driven ingestion framework, allowing new tables to be onboarded with minimal code changes
• Developed automated pipelines to load Avro data into Databricks volumes.
• Designed multi-layer architecture (Raw, Refined, Target) with incremental and historical load strategies and SCD Type 2 implementation.
• Implemented end-to-end data quality checks with alerts to ensure only validated data is processed.
• Processed data from multiple formats including CSV, Parquet, and Avro files.
• Worked on ETL modernization initiatives involving migration assessment of legacy Informatica PowerCenter workflows toward cloud-based integration solutions using Informatica Intelligent Cloud Services (IICS)

Data Engineering Sr Analyst at Accenture (2025-01 – 2025-10)

Informatica ETL Development Project

• Led ETL and data pipeline enhancements for a Legacy Database Migration Project which involved Data Schema change aimed at unifying legacy and static schema processing thus improving data loading performance.
• Worked with business users to translate business requirements into scaleable ETL mapping and workflow solutions while communicating solutions in a clear non technical manner.
• Developed scalable ETL mappings and workflows for enterprise data integration and schema transformation requirements.
• Built data validation and reconciliation using SQL, ensuring accuracy of large datasets and supporting incident resolution related to data discrepancies.
• Contributed to process improvements by automating manual ETL tasks and enhancing job scheduling, reducing turnaround time and improving operational efficiency.

Data Engineering Analyst at Accenture (2021-06 – 2024-12)

• Supported production ETL pipelines by analyzing session and workflow logs in IICS, identifying failure points, and applying fixes at mapping or data level without impacting upstream dependencies.
• Handled data load issues caused by source file inconsistencies (flat files and DB extracts), including schema mismatches, null violations, and duplicate records, by implementing validation checks and controlled reprocessing.
• Maintained and updated existing ETL mappings to accommodate incremental data changes (column additions, datatype changes), ensuring backward compatibility with downstream systems.
• Coordinated with QA and business users during UAT cycles to validate ETL outputs, track defects, and deploy fixes through controlled migration processes across environments.

Bachelor of Engineering in Computer Science – Sathyabama Institute Of Science & Technology (2017-08 – 2021-05)