Pradhuman Singh Parmar

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

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)

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.

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