Data Engineer
I am focused on data engineering fundamentals: designing ETL/ELT pipelines, writing clear SQL, modeling data for analytics, and building reproducible systems. I enjoy projects that involve messy raw data, transformation logic, orchestration, and making data trustworthy.
Skills
Grouped skills make it easy to scan the stack used across pipeline, modeling, and reliability work.
Projects
An evidence-first Python ingestion and QA pipeline that standardizes international university data from official sources into product-ready review datasets.
Stack
Python, Supabase, PostgreSQL, Playwright, Serper, OpenAI, Gemini, CSV, pytest
GitHub repository Read case studyA daily Airflow pipeline that extracts e-commerce product data, validates and loads market snapshots into PostgreSQL, and supports Metabase dashboards.
Stack
Python, Apache Airflow, Pandas, Pandera, PostgreSQL, SQLAlchemy, Metabase, Docker, pytest
GitHub repository Read case studyAn ELT lakehouse analytics platform for Formula 1 historical results, race sessions, telemetry, strategy, and weather insights.
Stack
Python, Apache Airflow, MinIO, PySpark, dbt, DuckDB, Metabase, Parquet, Docker
Read case studyCase Studies
Planning case study for a real-time transaction monitoring and fraud detection lakehouse using CDC, Kafka, Spark, Iceberg, dbt, Airflow, and Great Expectations.
In progressIn-progress case study for a Formula 1 lakehouse analytics platform that unifies historical results, race sessions, telemetry, weather, and strategy data into Bronze, Silver, and Gold datasets.
In progressIn-progress case study for an evidence-first university data ingestion and QA pipeline that turns official university source pages into auditable product review datasets.
ReadyReady case study for a daily Airflow ETL pipeline that collects e-commerce product data, validates quality, stores market snapshots in PostgreSQL, and powers Metabase dashboards.