Ved Prakash Shukla

Led end-to-end data engineering initiatives, including migrating legacy SQL procedures to Databricks by developing Python, PySpark, and Spark SQL notebooks, enabling seamless transition to modern, scalable processing for healthcare analytics. Designed and implemented Medallion architecture (Bronze, Silver, Gold layers) for data lakes, establishing layered data refinement pipelines that improved data quality and accessibility for downstream consumers. Orchestrated file ingestion workflows leveraging Azure Data Lake Storage (ADLS), Azure Data Factory (ADF) pipelines, and Databricks workflows with automated scheduling, processing 10M+ daily records while ensuring fault-tolerant and efficient data movement. Directed workflow orchestration via Azure Data Factory, implementing CI/CD pipelines that accelerated deployments by 40% and supported multi-environment scalability for revenue cycle management and value-based care systems. Championed Delta Lake adoption for ACID-compliant data lakes, establishing governance frameworks that mitigated risks and enabled audit-ready compliance for regulated industries, including physician key metrics and operational reporting.

Designed and automated enterprise-grade payroll reconciliation workflows, streamlining processes for 40K+ employees and reducing manual intervention by 50% through advanced scripting and monitoring. Engineered sophisticated reconciliation algorithms for high-volume credit datasets, boosting accuracy by 40% and minimizing financial discrepancies in a fast-paced fintech environment.

Orchestrated data ingestion pipelines from Google Analytics into Redshift clusters, optimizing query performance to cut reporting latencies by 60% for marketing intelligence. Integrated and harmonized 3+ disparate external data sources into unified dashboards, providing actionable insights that drove 25% uplift in campaign ROI for cross-functional stakeholders.

Optimized complex SQL queries and streaming pipelines for real-time dashboards, enhancing query throughput by 30% and supporting high-availability analytics for sales operations. Led proactive monitoring and tuning of AWS infrastructure, Kafka streams, and log analytics, reducing system downtime by 35% through predictive alerting and root-cause analysis.