Deepanshu Aggarwal

Led a 15-engineer cross-functional team using Agile & CI/CD and drove a 30% productivity boost and consistent on-time delivery.
Received the Q4 IMPACT Award in Jan 2025.
Architected a cost-optimized Delta Lake storage solution using Medallion Architecture, compression, and archival policies and reduced storage costs by 40% ($50K/year) while improving query performance.
Engineered a data quality and observability framework using automated profiling and improved analytics reliability by 35% and reduced pipeline downtime by 20%.
Built an Automated Monitoring & Alerting system using Python, Databricks, and ServiceNow that reduced MTTR by 25% and sustained 99.9% uptime SLA across all production workflows.
Designed fault-tolerant recovery playbooks and schema evolution strategies with dbt and Unity Catalog, enabling zero-downtime deployments.

Led a 15-engineer cross-functional team using Agile & CI/CD — drove a 30% productivity boost and consistent on-time delivery. Recognized with the Q4 IMPACT Award, Jan 2025., Architected a cost-optimized Delta Lake storage solution using Medallion Architecture, compression & archival policies — reduced storage costs by 40% ($50K/year) while improving query performance., Engineered a data quality & observability framework using automated profiling — improved analytics reliability by 35% and reduced pipeline downtime by 20%., Built an Automated Monitoring & Alerting system (Python, Databricks, ServiceNow) that reduced MTTR by 25% and sustained 99.9% uptime SLA across all production workflows., Designed fault-tolerant recovery playbooks and schema evolution strategies with dbt & Unity Catalog — enabling zero-downtime deployments.

Engineered 50+ production ETL pipelines using Python, SQL, and PySpark and 15+ reusable data accelerators, boosting throughput by 33%, processing efficiency by 28%, and saving $20K/year.
Migrated core datasets to Parquet with Delta Lake partitioning, increasing throughput by 33% and reducing storage costs by approximately 20%.
Tuned AWS integrations including S3, Redshift, and PostgreSQL, cutting data latency by 20%.
Built DQT (Data Quality Tool) automating column fill-rate analysis and cutting manual QA time by 40%.
Rewrote PySpark parsers improving ingestion speed by 70%.
Automated Power BI reporting saving 15+ hours per week.
Mentored 5 junior engineers through migration.
Achieved Top 5% in internal DE certification assessments.

Engineered 50+ production ETL pipelines (Python, SQL, PySpark) and 15+ reusable data accelerators — boosting throughput by 33%, processing efficiency by 28%, and saving $20K/year., Migrated core datasets to Parquet with Delta Lake partitioning — throughput up 33%, storage costs down ~20%; tuned AWS integrations (S3, Redshift, PostgreSQL) cutting data latency by 20%., Built DQT (Data Quality Tool) automating column fill-rate analysis and cutting manual QA time by 40%. Reduced time-to-detect data issues significantly., Rewrote PySpark parsers improving ingestion speed by 70%; automated Power BI reporting saving 15+ hrs./week; mentored 5 junior engineers through migration. Top 5% in internal DE certification assessments.

Developed PySpark & Pandas data cleansing and transformation scripts to improve analytics pipeline quality across client datasets. Designed fault-tolerant recovery playbooks and schema evolution strategies with dbt & Unity Catalog — enabling zero-downtime deployments.