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Shivakumar Thuppala

@shivakumarthuppala

Senior Data Engineer at AIG building governed insurance data pipelines and tuning Spark workloads for throughput and cost efficiency.

United States

Message

At AIG, I engineer and support batch and incremental pipelines for structured and semi-structured insurance data using Python, SQL, PySpark, and Databricks. I develop reusable ingestion patterns with validation and reconciliation, and tune Spark workloads to improve throughput and cost efficiency.

Previously, I built financial data pipelines at JPMorgan Chase & Co. and cloud pipelines for healthcare data at Optum. My selected technical impact includes a Teradata-to-Azure Databricks migration with reported improvements in transformation speed and query performance, and Spark and Delta Lake tuning that reduced pipeline runtimes and Databricks spend.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

AIG

Jul 2025 - Present (1 year 3 months)

Engineer and support enterprise batch and incremental data pipelines for insurance datasets using Python, SQL, PySpark, and Databricks. Develop reusable ingestion patterns with schema validation, reconciliation, and audit controls, and tune Spark workloads for performance and cost efficiency.

Python SQL PySpark Apache Spark Azure Databricks Delta Lake Data Pipelines ETL Data Quality CI CD

Data Engineer

JPMorgan Chase & Co.

Mar 2024 - May 2025 (1 year 2 months)

Built and maintained SQL, Python, PySpark, and Apache Spark ETL/ELT pipelines for banking and financial data. Developed source-to-target reconciliation checks and tuned Spark execution plans, partitions, joins, and shuffles.

SQL Python PySpark Apache Spark ETL ELT Data Reconciliation REST APIs SLA Monitoring

Data Engineer

Optum

Jun 2021 - Jul 2023 (2 years 1 month)

Built cloud data pipelines integrating healthcare claims, clinical, laboratory, and operational data. Authored Python, SQL, PySpark, and Spark SQL transformations for standardization, validation, and curation of high-volume healthcare datasets.