Software Engineer building production-grade data pipelines, ETL systems, and Lakehouse infrastructure.
Currently engineering an HVAC Fault Detection & Diagnostics (FDD) Engine designing the data layer for real time rule evaluation and analytics at scale.

• ETL & Incremental Pipelines — batch and streaming ingestion with exactly once semantics, schema governance, and audit trails
• Lakehouse Stack — Delta Lake tables, PySpark transformations, Databricks workflows; recently migrated large scale workflows from Pandas → Polars for significant performance gains
• Production Python Packages — modular, testable, cloud agnostic libraries (see open_auto_loader below)

A high-performance incremental data ingestion library built on Polars + Delta Lake.
Supports Local, AWS S3, Azure Blob, and GCP out of the box. SQLite-backed checkpointing ensures files are processed exactly once.

loader = OpenAutoLoader(
    source="s3://raw-bucket/incoming/",
    target="s3://silver-bucket/tables/users",
    format_type="csv"
)
loader.run(batch_id="daily_batch_001")

Core: Python · PySpark · Polars · SQL
Lakehouse: Delta Lake · Databricks · Azure Data Lake
Infra: Docker · FastAPI · PostgreSQL · MySQL · Azure
Cloud Storage: AWS S3 · Azure Blob · GCP GCS

Advanced Cloud Data Engineering — Azure Data Factory · Synapse Analytics · medallion architecture patterns

Open to collaborating on Data Engineering, MLOps, and AI pipeline projects.