Ajinkya Jadhav

I’m a results-driven Data Engineer with 5+ years of experience building scalable data platforms and high-performance ETL/ELT pipelines on AWS. I focus on producing reliable, analytics-ready datasets and engineering pipelines that perform under real-world volume.

In my current role, I designed and developed ETL/ELT pipelines processing millions of records daily using Python, SQL, and PySpark. I implemented incremental loading to cut processing time by 35% and optimized Spark jobs and complex SQL to improve performance by 40–60%.

I build modern cloud architectures, including data lake solutions on AWS S3 (raw to processed layers using Parquet) and distributed processing pipelines using AWS Glue. I automate workflows with Apache Airflow (scheduling, monitoring, retries) and add operational confidence through data validation, data quality frameworks, and AWS CloudWatch monitoring and alerting.

I’ve also architected end-to-end warehouse and event-driven pipelines—using event-driven AWS Lambda triggers for Glue jobs, loading into Amazon Redshift (including COPY from multiple sources), and designing warehouse schemas (star schema) for analytics. I collaborate closely with BI teams to deliver dashboards (Power BI) and ensure production stability through fixes like job sequencing and Glue scaling to handle high data volume spikes.