Headline

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

About

I didn't start out knowing I wanted to be a Data Engineer — I started by being genuinely curious about what happens to data after it's collected. That curiosity led me to build things: pipelines, validation frameworks, streaming systems, and eventually a deep appreciation for what it means to make data trustworthy at scale. Today I work as a Data Engineer at Neuleap.ai, where I design and maintain Medallion Architecture pipelines processing 2.1M+ pharmaceutical records every month. The work that matters most to me isn't just moving data — it's building systems that analysts and ML teams can actually rely on. Reducing manual data fixes from 25 per week to 5. Cutting ETL runtime by 50%. Helping downstream teams ship faster because the data layer doesn't let them down. Before this, at Knowledge Lens, I built a PySpark ETL system handling 200GB+ of monthly data and a real-time Kafka consumer enabling same-day analytics — which taught me that good data engineering is really about removing friction for the people who depend on it. I'm also exploring the intersection of data infrastructure and AI — building RAG systems with LangChain and OpenAI, understanding how production data pipelines need to evolve to serve LLM applications well. My core stack: Python · PySpark · Apache Kafka · Azure Databricks · AWS · PostgreSQL · MongoDB · Docker · Kubernetes · FastAPI · SQL

Location

Bengaluru

AI stack

hugging face

langchain

ollama

openai

Work experience

Data Engineer at NeuLeap.AI, Nov 2025 - Present · 8 mos, Pune District

SDE Intern at Rockwell Automation, Feb 2025 - Aug 2025 · 7 mos, Bengaluru, Karnataka, India, 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