Purna Chander Odela

8+ years of experience as Data Engineer with analytical programming using SQL, Python, Snowflake, AWS, and GCP. Hands-on experience in working with Amazon Web Services (AWS) cloud and its services like EC2, Lambda, S3, SQS, API Gateway, Event-Bridge, Kinesis, Glue, CloudWatch, CloudFormation, RedShift, Quick Sight, and other services of the AWS family.

Experience in developing very complex mappings, reusable transformations, sessions, and workflows using Informatica ETL tool to extract data from various sources and load into targets. Experienced in integration of various data sources (DB2-UDB, SQL Server, PL/SQL, Oracle, Teradata, XML and MS-Access) into data staging area.

Experience in designing and implementing HIPAA-compliant data pipelines using Apache Airflow and Python to securely ingest, transform, and store Protected Health Information (PHI) across multiple cloud storage platforms including AWS S3 and Redshift. Implemented data preprocessing frameworks to support LLM fine-tuning on custom corpora, using Python, DVC for version control, and data validation.

Senior Data Engineer - State Street - Boston, MA

(2025-06)

• Used AWS Athena extensively to ingest structured data from S3 into other systems such as RedShift or to produce reports.
• Designed and maintained scalable data pipelines integrating structured and unstructured data sources to support downstream AI/ML applications, utilizing Apache Spark, AWS Glue, and orchestration tools like Apache Airflow.
• Worked with Snowflake cloud data warehouse and AWS S3 bucket for integrating data from multiple source system which include loading nested JSON formatted data into snowflake table.
• Used Spark-Streaming APIs to conduct on-the-fly transformations and actions for creating the common learner data model, which receives data from Kinesis in near real time.
• Designed and implemented scalable ETL pipelines using AWS Glue, Python, Snowflake, and Amazon Redshift to process high-volume enterprise datasets.
• Developed real-time data ingestion and streaming applications using AWS Kinesis and Spark Streaming for downstream analytical platforms.
• Optimized complex SQL queries, reducing execution time by over 40% and improving reporting performance.
• Collaborate with data architects for data model management and version control.
• Worked closely with cross-functional Agile teams to deliver scalable cloud-based data engineering solutions.
• Configured Databricks workflows and automated data processing jobs for batch and streaming data pipelines.
• Worked on various modules for CSDM (Credit Card Services Data Mart) and USDM (U.S. Data mart) and RBDM module that reported production issue, which were part of SCR (Small Change Request).
• Capture technical metadata through data modeling tools. Create data objects (DDL).
• Built and deployed NLP pipelines for sentiment analysis and entity extraction using Hugging Face Transformers, spaCy, and fine-tuned LLMs like GPT-4, while ensuring real-time data ingestion through Kafka and AWS Kinesis.
• Performed end-to-end Architecture & implementation

Master's