With 2+ years of professional experience, I design and build production-grade data pipelines, data models, and transformation workflows. I deliver clean, analytics-ready data through warehouses, dashboards, and APIs that power real business decisions.
End-to-end data engineering: from raw ingestion to dashboards, APIs, and ML-ready data products.
Production-grade ingestion pipelines with data validation, incremental loads, backfills, Reverse ETL, and schema evolution. From raw sources to warehouse-ready tables, reliably.
Dimensional models, star schemas, and Medallion-layered transformations (Bronze to Silver to Gold). Converts raw data into query-optimized, analytics-ready assets with full lineage tracking.
Scalable cloud warehouses with partitioning, clustering, and row-level security. Connected to live dashboards and self-serve BI layers built for non-technical stakeholders.
REST and webhook APIs, autonomous agents, and ML model integration layers that operationalize machine learning outputs into downstream data pipelines and business systems.
The full stack I use to build production data systems, from ingestion to dashboards.
Python, SQL, Ad-Hoc SQL, JavaScript, TypeScript, Java, C
ETL / ELT, Reverse ETL, Data Modeling, Dimensional Modeling, Transformation, dbt, dlt, Airflow, Kafka, Apache Spark, CDC, Orchestration
BigQuery, Snowflake, Redshift, PostgreSQL, Star Schema, Data Lineage, Data Quality, Query Optimization, Partitioning, Clustering
Looker Studio, Apache Superset, Streamlit, REST APIs, FastAPI, Webhooks, ML Models, LangChain, OpenAI
GCP, AWS S3, Docker, Data Governance, Data Observability, Data Catalog, CI/CD, Row-level Security
Real-world projects spanning end-to-end pipelines, data modeling, transformation layers, dashboards, and cloud architectures.
Production-grade retail analytics pipeline using synthetic e-commerce data and a full Medallion architecture — from raw ingestion to Looker Studio dashboards.
Fully automated pipeline extracting Kaggle sales data via dlt, loading into BigQuery, and transforming to a business-ready Gold layer with dbt — orchestrated by Airflow.
Automated batch data pipeline ingesting S&P 500 ETF data from yfinance into AWS S3 via Medallion architecture, transformed with Apache Spark and visualised in Superset.
Daily batch pipeline ingesting sector ETF data, computing financial KPIs (moving averages, returns) using Spark, stored on S3 with Medallion layers and visualized live.
Let's discuss how customized data architecture can accelerate your business.