Looking for a job

Rushikesh Gajre

@rushikeshgajre

Data Engineer with 4+ years of experience building scalable ETL/ELT data pipelines using Python, SQL, PySpark, Apache Spark, Databricks, AWS, DBT, and

India

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What I'm looking for

Looking for Data Engineer / Senior Data Engineer opportunities where I can leverage my experience in Python, PySpark, SQL, AWS, Databricks, Snowflake, and ETL development. I am interested in challenging projects, opportunities to grow technically, and a collaborative work environment. Open to opportunities across India and available to join immediately.

At Luna Software Solutions, I build AWS ETL/ELT pipelines that process millions of records daily from multiple source systems into Redshift, improving analytics data availability. I've cut processing time by 35–40% through CDC and watermarking frameworks, automated 10+ pipelines with Airflow, and reduced report generation from 24 hours to under 4 hours.

I work across Python, PySpark, SQL, Databricks, Snowflake, Redshift, and AWS to deliver distributed transformations, data models, quality controls, and monitoring. My healthcare data pipeline project moved clinical-record processing from daily batch to near real-time while reducing runtime by 40%.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Luna Software Solutions Pvt Ltd

Jun 2023 - Present (3 years 2 months)

Architected end-to-end ETL/ELT pipelines on AWS using Python, PySpark, and SQL, processing millions of records daily from multiple source systems into Redshift. Built incremental load frameworks with CDC logic, reducing processing time by 35-40% and improving pipeline reliability.

Data Engineer

Luna Software Solutions

Dec 2022 - Jun 2023 (6 months)

I gained hands-on experience in building and maintaining data pipelines using Python, SQL, and PySpark. I worked on data extraction, transformation, and loading processes, performed data validation and quality checks, and supported ETL pipeline development. This internship helped me build a strong foundation in data engineering and prepare for working on scalable.