Senior Data Engineer with 7+ years experience building cloud-native data pipelines. Specialized in optimizing Spark workloads, real-time streaming, and cloud data warehouses to drive data-driven decisions.
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Engineered cutting-edge streaming data pipelines to revolutionize product lifecycle tracking, seamlessly integrating PySpark on AWS Glue with Iceberg for next-level efficiency. Proactively optimized performance, slashing infrastructure costs and operational overhead, driving a resilient, future-ready data ecosystem. 🚀
Orchestrated a high-impact data pipeline, leading a team of three to revolutionize actuarial data processing. Leveraged AWS Glue, Redshift, and an on-prem SQL warehouse to craft a seamless, high-performance architecture. Engineered data transformation logic in PySpark and Spark-SQL, slashing processing time by 90% over legacy Excel workflows. Recognized with Applause and Spot Awards for technical excellence and innovation.
Architected a scalable Databricks-powered data pipeline leveraging PySpark and ML to process 20TB of user feedback (text & audio) on drug adverse effects monthly. Integrated AWS Aurora for seamless data access, driving advanced analytics and visualization. Enabled ~$75M in business value by enhancing drug safety analysis. Recognized as the firm’s most valuable project.
Automated daily water and air quality index calculations for Indian cities, eliminating manual processes and reducing processing time by 75% while enhancing data accuracy. Developed a seamless government bid submission portal, boosting project bids by 20% and streamlining public sector engagements
I love exploring the outdoors whenever I get the chance. Whether it's riding my motorcycle through scenic routes, trying new foods from different cultures, or traveling to experience the world firsthand, adventure fuels my passion.
When indoors, I indulge in sci-fi and fantasy movies, experiment with new recipes as an aspiring chef, and stay up to date with the latest advancements in data engineering and cloud technologies.