Headline

Data Engineer | PySpark · Azure Databricks · Kafka · Medallion Architecture | Healthcare Analytics | Open to Opportunities

About

Data Engineer with 1.5+ years of experience building production-grade ETL/ELT pipelines on PySpark, Azure Databricks, and Apache Kafka. I design Medallion Architecture pipelines that process 2.1M+ pharmaceutical records monthly, and I care most about making data trustworthy enough that analysts and downstream teams never have to second-guess it. At NeuLeap.AI (deployed at ZS Associates, working on oncology analytics for a top-10 global pharma client), I've reduced manual data fixes from 25 per week to 5 and cut ETL runtime by 50% through pipeline redesign and validation frameworks built directly into the data layer. Previously at Knowledge Lens (a Rockwell Automation company), I built a PySpark ETL system handling 200GB+ of monthly data and a real-time Kafka consumer enabling same-day analytics — work that taught me good data engineering is fundamentally about removing friction for the people who depend on the data. Core stack: Python · PySpark · Apache Kafka · Azure Databricks · Delta Lake · Apache Airflow · PostgreSQL · MongoDB · Docker · Kubernetes · FastAPI · SQL Currently exploring opportunities in Data Engineering roles across Bengaluru, Hyderabad, and remote-India positions.

Location

Bengaluru

AI stack

antigravity

claude code

claude

cursor

langfuse

langgraph

Work experience

Data Engineer at NeuLeap.AI, Nov 2025 - Present · 11 mos, Pune District

Data Engineer at Rockwell Automation, Feb 2025 - Aug 2025 · 7 mos, Bengaluru, Architected the Factory Data Mosaix Model using Azure Data Factory, Azure Databricks, Apache Kafka, FastAPI, and PySpark, enabling real-time data ingestion, transformation, and low-latency analytics for industrial clients—resulting in a 60% reduction in data processing latency. Improved platform scalability and reliability by deploying containerized FastAPI microservices with Docker and orchestrating them via Kubernetes, while streamlining CI/CD using Git. This reduced deployment failures by 15% and accelerated release cycles by 20%.

Education

Bachelor of computer science and engineering, Computer Science, NIST University