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Rahul Vundamodugula

@rahulvundamodugula

Data Engineer at Tata Consultancy Services improving data quality across pipelines processing 56 billion daily events.

At Tata Consultancy Services, I developed data quality checks and validation rules in ETL workflows, identifying and resolving over 15,000 anomalies monthly. I also optimized Apache Spark SQL queries processing 56 billion daily events while maintaining data accuracy and PII compliance.

At Clemson University, I built Python validation workflows for sensor data and implemented quality and privacy checks that reduced data inconsistencies by 30%. I also automated Terraform and AWS CloudFormation deployments for secure cloud environments.

At Accenture, I built secure data pipelines with PII controls and improved data quality using SQL validation, reducing defects in transactional datasets by 35%. I also optimized Snowflake queries and supported containerized deployments with audit logging.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Tata Consultancy Services

Sep 2025 - Present (1 year)

Developed data quality assurance checks and validation rules within ETL workflows, identifying and resolving over 15,000 anomalies monthly. Optimized Apache Spark SQL queries processing 56 billion daily events, ensuring data accuracy and strict PII handling compliance.

Data Quality Assurance Data Validation ETL Apache Spark SQL PII Handling Data Privacy Audit Logging Kafka Data Reconciliation

Application Data Developer

Clemson University

Jan 2025 - Aug 2025 (7 months)

Built Python data validation workflows that processed over 5 TB of sensor data daily, ensuring business datasets met accuracy standards. Automated Terraform and AWS CloudFormation deployments to create secure cloud environments.

Python Data Validation Data Quality Assurance Terraform CloudFormation Data Privacy Anomaly Detection

Data Engineer

Accenture

Aug 2021 - Jun 2023 (1 year 10 months)

Built secure data pipelines with embedded PII handling controls, ensuring strict adherence to data privacy protocols. Improved data quality assurance by identifying and resolving anomalies using SQL validation, reducing defects in transactional datasets by 35%.