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Devesh Poojari

@deveshpoojari

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Data engineer specializing in cloud-native ETL, Spark, Airflow, and data warehouse optimization.

India

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What I'm looking for

I seek roles where I can design scalable cloud-native ETL and data warehouses, drive performance optimizations, and collaborate with analytics teams to deliver actionable insights while growing in cloud and data architecture responsibilities.

I am a data engineer with over 4 years of experience building scalable ETL pipelines and data warehouses across AWS and GCP. I focus on designing cloud-native, serverless pipelines that improve data availability and reduce operational cost.

At my current role I led a migration from AWS to GCP that cut cloud costs by 20% and developed 50+ ETL pipelines using Cloud Run, Pub/Sub, Dataproc, and BigQuery, improving data availability by 40%.

I optimize performance through Apache Spark, SQL tuning and workflow orchestration with Airflow, achieving query and pipeline execution improvements of about 40% and boosting BI data quality by 30%.

I pair technical execution with business alignment—designing data warehouse models, building analytics solutions and delivering insights that drive outcomes like a 22% increase in customer retention and faster stakeholder decision-making.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Brandonline

Jun 2022 - Present (3 years 4 months)

Led migration from AWS to GCP reducing cloud costs by 20% and developed 50+ ETL pipelines on GCP, improving data availability by 40% and query performance by 40%.

BigQuery Pub Sub Apache Spark Python SQL Airflow Data Modeling

Data Scientist

Brandonline

Jun 2020 - May 2022 (1 year 11 months)

Built data migration tooling that cut migration time by 35% and developed ML models that increased customer retention by 22%; integrated AWS services to enable 50% better data accessibility.

Python Machine Learning AWS EC2 S3 Athena SQL REST APIs A B Testing

Data Analyst Intern

Brandonline

Nov 2019 - May 2020 (6 months)

Automated workflows with Python, SQL, and Airflow saving 14 hours/week and fulfilled 95% of ad-hoc dashboard requests within SLA to accelerate stakeholder decisions.