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Sagar U

@sagaru

Data Engineer with 4+ years building scalable ETL/ELT pipelines (Python, PySpark, SQL, AWS, Airflow) for aerospace & defense data platforms.

India

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What I'm looking for

Looking for a Data Engineering role where I can build and scale reliable ETL/ELT pipelines on modern cloud infrastructure. I’m drawn to teams that value data quality and clean architecture, and I’d love to keep working with Python, PySpark, SQL, and AWS while growing into more complex, high-impact data platform challenges.

I’m a Data Engineer with over four years of experience building and optimizing pipelines that turn large-scale, messy data into something teams can trust. Most of my work has been in aerospace and defense, processing aircraft telemetry, satellite imagery, and communication logs, where data quality is critical to real decisions.

Day to day, I design ETL/ELT pipelines in Python, PySpark, and SQL, run distributed batch jobs on Apache Spark, and orchestrate workflows through Airflow on AWS services like S3, Glue, and Redshift. I care about pipelines that don’t silently fail, datasets that are validated before anyone builds on them, and queries fast enough that analysts aren’t left waiting.

Currently open to Data Engineer, ETL Developer, and Big Data roles where I can work on scalable, high-impact data platforms.

Experience

Work history, roles, and key accomplishments

Data Engineer

LaunchTrax Pvt Ltd

Jun 2022 - Mar 2026 (3 years 9 months)

Data Engineer with experience building scalable ETL/ELT pipelines using Python, SQL, PySpark, Spark, Airflow, and AWS. Processed 10M+ records daily from 15+ sources, reduced processing time by 40%, improved SQL performance by 45%, and automated data workflows reducing manual effort by 60%. Built reliable cloud data solutions on AWS with 99.9% availability and 99.5% pipeline reliability.

Data Engineer

LaunchTrax Pvt Ltd

Nov 2021 - May 2022 (6 months)

Data Engineer Intern with experience supporting ETL/ELT pipelines using Python, SQL, PySpark, and AWS. Assisted in processing 500K+ records for analytics, managed 500GB+ of data in Amazon S3, optimized Spark workflows improving efficiency by 15%, and collaborated with engineering teams to resolve pipeline issues and maintain code quality using Git.