Ayudh Singh

Background

About Ayudh Singh

build scalable, reliable, and high-performing data platforms on Google Cloud that power real-time business decisions.With 6+ years of experience as a Data Engineer, I specialize in designing end-to-end data architectures using BigQuery, Airflow, Pub/Sub, Datastream, and Python. I’ve helped enterprises modernize their data ecosystems by migrating legacy warehouses to GCP, building streaming data pipelines, and implementing data quality frameworks that bring trust to analytics.I don’t just move data—I engineer data systems that are fast, cost-efficient, observable, and built for scale. What I’ve Done Migrated 5+ on-premise data warehouses to BigQuery, reducing cloud costs by 35% and improving query performance by 60% Built real-time CDC pipelines using Datastream + Cloud Functions, ensuring 99.9% accuracy across transactional systems Engineered streaming ingestion with Pub/Sub, handling 2TB/day data with sub-minute latency Developed data quality & lineage framework using BigQuery stored procedures + metadata layer + Looker dashboards Orchestrated ETL/ELT workflows in Airflow, increasing pipeline reliability by 40% Optimized BigQuery storage + compute using clustering, partitioning, materialized views, and cost controls Automated Python-based data cleansing and validations, saving 20+ engineering hours weekly Tech StackGCP (BigQuery, Airflow, Pub/Sub, Datastream, Cloud Functions, GCS) · Python · PySpark · SQL · ETL/ELT · Data Modeling · Data Quality · Data Architecture · Looker Studio · API Integration · Git/Bitbucket What I BringI approach data engineering with: Engineering mindset → build once, scale forever Problem solving → optimize cost, latency & reliability step by step Clean architecture → design for automation, monitoring, and reproducibility Business impact → build data systems that drive real outcomes