Analytics Engineer
I build analytics-ready data platforms from raw ingestion to dashboard-ready gold layers. My work spans ETL/ELT pipeline design, dimensional data modeling, and BI development, with a growing focus on modern analytics engineering practices like medallion architecture, workflow orchestration, and transformation-as-code.
I care about data that's trustworthy, fast, and easy for stakeholders to act on whether that's a star schema powering a Power BI suite, or a dbt-modeled warehouse feeding downstream reports.
Quick summary: 1.5+ years turning messy, multi-source data into governed, automated reporting systems cutting manual reporting effort by up to 40% and building pipelines that scale.
Databricks Medallion Warehouse End-to-end medallion architecture in Databricks using Apache Airflow and dbt Core — automating data movement across Bronze, Silver, and Gold layers with reusable transformation models and validation checks.
Bike Store Relational Database & Analytics PySpark incremental ETL pipeline from MongoDB to PostgreSQL with automated schema evolution and SQL-based data quality testing, plus 21 analytical SQL reports on sales, customers, and inventory.
Walmart Medallion Data Pipeline Production-style MongoDB → PostgreSQL → dbt pipeline with incremental PySpark extraction, a tested medallion warehouse (Bronze/Silver/Gold), orchestrated with Airflow and containerized with Docker.
Experience across editorial analytics (Medianama) and insurance BI (Bajaj Allianz General Insurance) building automated Power BI dashboards, SQL-based reporting pipelines, and star/fact-dimension data models in PostgreSQL for stakeholder and leadership reporting.
A snake that eats its way through my GitHub contribution graph.
Open to analytics engineering and data analyst roles always happy to talk data modeling, pipelines, or BI.