Advancing the fusion of AI and data products & platforms, translating complex data ecosystems into intelligent, AI-powered platforms that empower analytical velocity, engineer productivity, and organizational intelligence.

• Driving Agentic Data Products & Platforms: Build AI-driven, adaptive data systems by standardizing contextual data layers using RAG, MCP, and semantic data indexing. Integrate agent reasoning with business-aware semantics to improve data quality and accelerate decision-making at scale. Example projects include the Pinterest Analytics Agent capable of semantic-based search for data entities, Text-to-SQL, answering analytical questions, etc. And an AI-powered table documentation system for thousands of analytical tables, raising standardized table query usage from 20% → 80% and saving 1,000+ engineer hours.

• Accelerated business insights: Redesigned top-line metrics pipeline (MAU, DAU), cutting compute by 40% and storage by 85%, simplifying spam removal, and delivering critical metrics 5+ hours earlier daily. Led migration/deprecation of 500+ downstream jobs.

• Established company-wide data governance: Founding member of the Data Governance Working Group; drove cross-functional standards, tooling, and monitoring frameworks, boosting trust, velocity, and collaboration across all data teams.

• Leading and mentoring 10+ engineers through these initiatives.

As a Senior Software Engineer in the Data Insights team of Criteo Retail Media Platform, I played a pivotal role in both backend and frontend development.

Backend Development:

• Designed and built robust Spark data pipelines to transform raw user interaction and retailer data into structured reporting datasets.

• Implemented an upgraded version of Criteo's ad attribution algorithm, capable of scaling to billions of ad records, including orders data and user interaction data.

• Conducted training sessions for the internal data analysts team on querying these datasets and provided comprehensive documentation.

Frontend Development:

• Developed multiple dashboards using React, offering valuable insights into Ad campaign performances for both advertisers and retailers. These dashboards have been instrumental in driving data-driven decision making.

In my role as a Senior Application Developer at the University of Michigan, I was instrumental in the development of key applications that significantly improved data analysis and exploration.

1. Created the MBNI Analysis Hub (AHub), and its subsequent cloud-based version, AHub Cloud. These platforms bridge the gap between hypothesis development and complex data analysis. They provide a user-friendly web interface and leverage cloud-based automated data pipelines to handle large volumes of genomic data.

2. I contributed significantly to the development of CoolMap, an interactive application designed for efficient exploration of large datasets. This tool presents data in human-manageable views with biological context such as gene/disease ontologies, and allows for quick drill-in for details related to visual patterns. This application has greatly enhanced the ability to understand and interpret complex data sets.

Development of networking features and corresponding CLI for VMware ESXi.