Jaya Teja Challagundla

• Designed and implemented data ingestion pipelines from multiple sources including APIs, CSV/Parquet files, and AWS S3, leveraging AWS Glue for transformation workflows.
• Built data models in AWS Athena and Azure Data Factory for integration with downstream BI tools.
• Developed Python validation scripts to automate data integrity checks, schema matching, and anomaly detection prior to loading into analytics environments.
• Configured Role-Based Access Control (RBAC) in Tableau Server and IAM policies in AWS to ensure secure, compliant access to datasets.
• Created Power BI dashboards with DAX measures and Row-Level Security (RLS) to deliver personalized insights for different business units.
• Implemented incremental ETL processes in Airflow, reducing daily processing time by over 35%.
• Assisted in setting up CI/CD workflows using GitHub Actions to automate deployment of data pipeline code.
• Conducted cross-table reconciliation using SQL to ensure consistency between staging and production datasets.

Key Achievements:

• Reduced dashboard refresh latency from 30 minutes to under 5 minutes by optimizing ETL and data modeling.
• Improved data governance by standardizing naming conventions and access policies across multiple AWS services.

• Assisted in designing and implementing ETL pipelines to extract, transform, and load structured and semi-structured data from multiple sources into a centralized database.
• Worked with Apache Spark and PySpark for large-scale batch data processing and optimization of transformation jobs.
• Contributed to data quality checks by implementing validation scripts in Python and SQL, ensuring accuracy before loading into analytics systems.
• Automated data ingestion workflows using Apache Airflow, improving pipeline reliability and reducing manual intervention.
• Created data profiling reports to assist senior engineers in identifying anomalies and inconsistencies.
• Participated in building proof-of-concept solutions for real-time data streaming using Kafka and AWS Kinesis.
• Collaborated with cross-functional teams, including business analysts and QA engineers, to translate business requirements into technical data solutions.
• Documented workflows, transformation logic, and deployment steps, enabling smooth handover to production teams.

Key Achievements:

• Reduced ETL job runtime by 20% through query optimization and data partitioning strategies.
• Successfully contributed to the deployment of a streaming data proof-of-concept that was later adopted in a production use case.