Vivek Jain

I’m a Data Engineer with 5+ years of experience building scalable, high-performance data pipelines using PySpark, Azure Databricks, and SQL. I specialize in processing large-scale datasets (10M+ records/hour) and tuning distributed systems for both performance and cost efficiency. My work has improved pipeline performance by 30% and reduced cloud costs by up to 25%.

I build end-to-end ETL/ELT workflows across modern lakehouse patterns, including Delta Lake and Data Lakehouse architecture with Medallion (Bronze-Silver-Gold) design. I focus on reliable ingestion, strong data modeling, and production-ready data warehousing so teams can trust analytics-ready datasets. For orchestration, I’ve used Apache Airflow and Azure Data Factory (ADF) to automate workflows and strengthen scheduling and execution reliability.

In my recent role, I designed and deployed ETL pipelines on Azure Databricks, implemented Delta Lake for ACID transactions and optimized storage, and orchestrated end-to-end workflows via ADF and Airflow. I also engineered ingestion from REST APIs, web scraping, and Azure Blob Storage, while optimizing Spark jobs through partitioning and caching to cut execution time by 30%. I’ve added validation and quality checks and monitored cloud resource utilization to reduce infrastructure costs by 15–25%.

Beyond core pipeline engineering, I’ve delivered developer-friendly integrations and analytics support—building REST APIs with Flask, and creating interactive dashboards using Power BI and Tableau. I bring a practical, engineering-first mindset that balances correctness, scalability, and measurable business impact.