Raghav Jaju

I am a data engineer with hands-on experience designing and operating scalable data pipelines across GCP and Azure ecosystems. I focus on building reliable streaming architectures, implementing medallion-style transformations, and optimizing storage and query costs for analytics.

At my current role I redesigned an event ingestion pipeline to a Pub/Sub → Dataflow streaming architecture, eliminating data loss and enabling real-time ingestion into BigQuery. I reduced BigQuery costs by ~80% through partitioning, clustering, and better table design.

I implement DBT models and validation checks, enforce schema governance, and collaborate with product and analytics teams to deliver curated gold-layer datasets and automated dashboards. I have experience with Dataplex concepts, Airflow, PySpark on Databricks, Dataproc, and Vertex AI for model training and deployment.

I automate deployments with Bash, optimize ETL performance, and have solved practical problems such as compressing large video assets and migrating data across cloud services. I aim to build reliable, cost-effective data platforms that drive product and business insights.