Anirudh Sharma

I’m a Data Engineer with a passion for building systems that actually work at scale. Over the past few years, I’ve moved from general software engineering into data infrastructure—designing pipelines, optimizing queries, and solving the unglamorous but critical problems that keep data flowing reliably.

I’ve worked on everything from high-stakes financial data systems at S&P Global to building modular ETL frameworks that cut onboarding time in half. What drives me is the intersection of solid engineering practices and real-world data challenges: how do you make pipelines resilient? How do you reduce latency without reinventing the wheel? How do you catch data quality issues before they become disasters?

I’m also currently pursuing an M.Sc. in Financial Engineering at WorldQuant University, because I’m genuinely interested in where data and finance intersect.

I code in Python and SQL, work with Spark and dbt, and I’m comfortable with cloud platforms and DevOps tooling. But mostly, I enjoy solving concrete problems with clean, maintainable solutions.