Rahul Salunke

I’m a Data Engineer with 3+ years of experience designing and developing scalable ETL pipelines, handling big data workloads, and building data warehousing solutions. I focus on cloud-based data transformation, optimization, and pipeline reliability, using Python, PySpark, and SQL to deliver consistent, high-quality datasets.

In my recent work, I’ve designed multi-source integrations to migrate data from Teradata, AWS RDS MySQL, and GCS to BigQuery, using Dataproc, DataStream, and Cloud Functions for both batch and real-time ingestion. I orchestrate and monitor complex workflows with Google Cloud Composer (Airflow), supported by proactive alerting via Cloud Monitoring, and I optimize BigQuery queries to improve BI and reporting performance. I’ve also built analytics-ready enterprise pipelines—using SQL, Hive tables, PySpark, and Data Fusion/Dataproc—while cleansing, validating, and modeling data for efficient storage and access.

I thrive in end-to-end ownership: gathering requirements, drafting BRDs, extracting and automating ingestion from sources like MySQL and unstructured data (via Apache NiFi), and implementing ETL with AWS Glue and PySpark when needed. My approach is practical and performance-driven—standardizing data models (including Redshift-based warehousing), validating integrity across pipeline stages, and ensuring workflows run safely in production.