Prashanth Kumar Gardhas

Senior Data Engineer

Patton Labs Inc

Dec 2025 - Current (10 months)

Client: Blue Cross Blue Shield of Michigan Designed, implemented, and supported scalable ETL and big-data platforms to ingest, transform, and deliver data from diverse source systems into Hive, PostgreSQL, Amazon Redshift, and Databricks. Built automated ingestion pipelines for multiple file types stored in Amazon S3 and developed high-performance Spark ETL workflows using PySpark and Scala. Led SQL database migrations to AWS Redshift, leveraging Apache Spark for transformation and AWS Athena for efficient querying of data in S3. Implemented Slowly Changing Dimensions (SCD Type 1 and Type 2) to ensure historical data accuracy and regulatory compliance. Optimized large-scale data processing through Spark SQL tuning, partitioning, caching, an

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Senior Data Engineer

Patton Technology Labs

Apr 2025 - Dec 2025 (9 months)

Client: Blue Cross Blue Shield of Michigan Designed and delivered scalable, cloud-based ETL and data processing solutions on AWS, enabling reliable ingestion, transformation, and analytics across multiple data platforms. Led database migrations to Amazon Redshift, implemented robust data modeling strategies (including SCD Type 1 & 2), and built high-performance Spark pipelines integrated with Databricks. Automated and optimized large-scale data workflows to improve efficiency, reliability, and business insight generation.

Senior Data Engineer

Patton Labs Inc

Jan 2022 - Apr 2025 (3 years 4 months)

Implemented API-driven data ingestion using REST APIs, validating and testing endpoints with Postman, and integrating API calls into automated Airflow workflows for incremental and full data loads. Orchestrated data pipelines using Apache Airflow (DAGs) on Google Cloud Platform (GCP), integrating Cloud Storage buckets and BigQuery for scalable data ingestion and analytics. Designed and implemented scalable ETL frameworks and automated pipelines to ingest, transform, and load data into Hive, PostgreSQL, Redshift, and Databricks. Led SQL database migrations to AWS Redshift, leveraging Spark for data transformations and Athena for S3 querying. Built Spark-based ETL pipelines with PySpark/Scala, optimizing performance through partitioning, cach

Senior Data Engineer

phData

Jul 2021 - Mar 2025 (3 years 9 months)

Developed an end-to-end streaming pipeline to consume data from Kafka, dynamically call external APIs, and load processed responses into Databricks Delta tables for analytics. Migrated legacy data from Oracle and PostgreSQL to a unified Delta Lake, optimizing query and job performance. Designed and implemented an event-driven data pipeline using AWS Kinesis, Lambda, and Step Functions to process real-time CDC events with high throughput and fault tolerance. Developed AWS Lambda functions (Python) to validate, transform, and enrich streaming data from Kinesis before persisting it to downstream systems. Orchestrated complex data workflows using AWS Step Functions, enabling retry logic, error handling, and monitoring for reliable end-to-end pr

Data Engineer

Patton Labs Inc

Oct 2019 - Jul 2021 (1 year 10 months)

Contributed to the design and implementation of enterprise ETL frameworks for ingesting, processing, and extracting data from diverse source systems into Hive, PostgreSQL, and Amazon Redshift. Built and optimized Spark-based data pipelines using Scala and Spark SQL, supporting multiple file formats and compression techniques. Implemented historical and incremental data loads, automated workflows using Oozie, and applied performance tuning strategies in Hive for efficient large-scale data processing. Collaborated closely with business stakeholders in an Agile environment to deliver reliable, production-ready data solutions.

Software Developer

NTT DATA

Nov 2017 - Aug 2018 (10 months)

Worked on the Hanover Insurance Group project, managing data ingestion from multiple sources and loading structured and unstructured data into target tables. Optimized Hive queries for performance tuning and utilized Spark SQL for efficient data processing. Converted Hive/SQL queries into Spark transformations and leveraged partitioning and bucketing techniques for optimized data distribution. Imported MySQL tables into Hive using Sqoop and worked with various file formats, including Avro, ORC, JSON, and CSV. Experienced in Agile environments, version control with Git, CI/CD with Jenkins, and monitoring Hadoop clusters.

Prashanth Kumar Gardhas

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Prashanth Kumar Gardhas

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