Saroj Upreti

About Saroj Upreti

I’m a results-driven Data Engineer with over 7 years of experience designing and building scalable data solutions in cloud environments including AWS, Azure, and GCP. I specialize in end-to-end data pipeline development, real-time streaming, big data processing, and cloud data warehousing to power analytics, business intelligence, and machine learning initiatives. With a strong foundation in tools like Databricks, dbt, Airflow, Snowflake, and Spark, I help organizations turn raw data into reliable, actionable insights. I’m passionate about automation, performance optimization, and data quality — always striving to deliver solutions that are efficient, secure, and business-aligned.Key Achievements* Architected enterprise-scale Data Lakes and real-time pipelines across AWS, Azure, and GCP platforms.* Built high-performance ETL/ELT workflows using tools like ADF, Glue, Dataflow, and dbt for analytics at scale.* Successfully migrated legacy systems to cloud data warehouses (Snowflake, Redshift, Synapse), improving scalability and reducing costs.* Developed production-grade data pipelines supporting healthcare (HIPAA/FHIR), retail, and financial data systems.* Delivered impactful dashboards and reporting tools using Power BI, Tableau, and Looker to support business decisions.* Automated infrastructure with Terraform and Airflow, boosting deployment speed and workflow efficiency.* Improved data query performance by up to 35% through SQL tuning, partitioning, and clustering strategies.

Experience

1. Data Engineer

   JPMorganChase

   Nov 2022 — Present · Concord, NC, US

   At JPMorgan Chase, I lead the development of scalable data engineering solutions on Azure, leveraging tools like Azure Data Factory, Synapse Analytics, Databricks, and dbt to build robust, high-performance data pipelines. My work enables real-time analytics, supports machine learning workflows, and delivers trusted insights through modern BI platforms like Power BI and Tableau. I also focus on automation, infrastructure as code, and data quality governance to ensure efficiency, compliance, and operational excellence across financial data systems.Key Contributions* Built and automated Azure Data Factory pipelines for seamless, scalable ETL across cloud and on-prem data sources.* Created analytics-ready datasets using dbt and optimized data models in Azure Synapse and PostgreSQL.* Engineered real-time processing pipelines in Databricks with Scala, ensuring low-latency data delivery.* Automated infrastructure provisioning with Terraform, accelerating environment setup and standardization.* Developed batch data workflows in Java, boosting data throughput by 25%.* Implemented Hive partitioning and Hadoop cluster tuning for high-performance analytics workloads.* Delivered dynamic dashboards with Power BI and Tableau, supporting data-driven decisions across teams.* Applied robust data quality checks, row-level security, and compliance protocols to meet financial data regulations.* Supported ML workflows by building scalable, reliable pipelines for feature engineering and model deployment.* Led successful migration of legacy systems to Azure Synapse, improving scalability and reducing maintenance.

Education

• University of North Carolina at Charlotte

  Bachelor's degree, Data Science