Muhammad Mirza F.

Data Engineer specializing in Python ETL pipelines for cloud data warehouses. I build automated data engineering solutions processing millions of records daily — from real-time trading data to healthcare analytics — and increasingly the AI/RAG pipelines that sit on top of them. With 6+ years of experience, I design production ETL systems on GCP and AWS using Apache Airflow, Dagster, and modern data stacks: schema-change handling, data quality checks, and monitoring that means no more 3 AM pipeline pages. WHAT I DO ETL Pipeline Development I design scalable pipelines in Python with Airflow or Dagster — automated error handling, data validation, and monitoring built in from day one, not bolted on after the first outage. Cloud Data Engineering (GCP & AWS) GCP: BigQuery, Dataflow, Cloud Functions, Pub/Sub AWS: Redshift, Glue, Lambda, S3, Kinesis Data Warehouse Migration Migrated a legacy warehouse to GCP, cutting ETL processing time by 60%. AI/LLM Data Pipelines Retrieval-augmented generation (RAG) pipelines — document ingestion, chunking, vector search, and streaming LLM responses — for teams that need their data pipelines to feed an AI product, not just a dashboard. KEY PROJECTS - Election Data Pipeline — Python + Dagster + ClickHouse pipeline extracting campaign finance data, automating 40 hours/week of manual work. - Real-Time Trading Pipeline — Multi-layer architecture for algorithmic trading with <100ms latency and 100% uptime. - Healthcare Data Migration — Led an enterprise ETL migration to GCP BigQuery, cutting processing time by 60%. - ML Feature Engineering (AirAsia) — End-to-end pipeline feeding a recommendation system, processing millions of records and lifting engagement 25%. - AI Document Chat (RAG) — Built a local/cloud retrieval-augmented generation app (Python, ChromaDB, FastAPI/React) supporting both local (Ollama) and cloud (Anthropic, OpenAI) models, with streaming answers and live web-search augmentation. See portfolio. - Web Scraping ETL — Automated scraping-to-warehouse pipeline in Python and Airflow. - AWS Cloud Stabilization — Improved stability and performance of AWS systems: S3, Lambda, and webhook integrations. - Manufacturing Data Platform — Extended a manufacturing data platform across SQL Server, Azure SQL, and ERP data mappings. TECH STACK ETL & Orchestration: Apache Airflow, Dagster, Apache Spark, Kafka, dbt AI/LLM: RAG pipelines, vector databases (ChromaDB, Pinecone), OpenAI/Anthropic APIs Languages: Python (expert), SQL (expert) Cloud: GCP (BigQuery, Dataflow), AWS (Redshift, Glue, Lambda) Databases: PostgreSQL, ClickHouse, MongoDB, BigQuery, Redshift, Snowflake Python: Pandas, PySpark, SQLAlchemy, Apache Beam Infra: Terraform, Docker, Kubernetes, CI/CD IDEAL PROJECTS - ETL pipeline development from scratch (Python, Airflow, cloud platforms) - Data warehouse engineering and cloud migration (BigQuery, Redshift, Snowflake) - Real-time / streaming data engineering - Legacy ETL modernization - RAG and AI data pipeline engineering - Data integration across multiple sources - ML feature-engineering pipelines Available for fixed-price projects and long-term contracts. I work across US, European, and Asian time zones (GMT+7) and respond within 2 hours during business hours.