Indir - Data Engineer Intern

Demonstrated experience with Microsoft Azure.

Demonstrated experience with Google Cloud Platform.

Demonstrated experience with Python.

Demonstrated experience with SQL.

Demonstrated experience with PySpark.

Built a validated 60M-row analytical data warehouse in Google BigQuery from over 500M raw records, using a fault-tolerant Python ELT layer on GCP (BigQuery, Cloud Storage) ingesting 24GB+ of structured and unstructured data (CSV, JSONL, archived files).. Developed and optimized multi-phase SQL (joins, CTEs, window functions, query optimization) reconciling 9.6M+ records across heterogeneous sources, with automated data-quality validation and integrity checks catching defects before downstream consumption.. Deployed a FastAPI microservice with API-key authentication and audit logging to serve curated warehouse data to downstream consumers.

Designed and delivered automated Python ETL/ELT pipelines and database solutions for international clients, with retry/backoff, structured logging, and validation; gathered technical requirements directly from clients and delivered to deadline with minimal supervision

Built an end-to-end serverless ML pipeline (Pearls AQI Predictor) forecasting Air Quality Index up to 72 hours ahead for any city. Used a dual-mode Hopsworks/SQLite feature store, leak-free training with TimeSeriesSplit CV across Ridge, Random Forest, and HistGradientBoosting, SHAP interpretability, and a Streamlit dashboard with automated hourly/nightly retraining via GitHub Actions.