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Vijay Alluri

@vijayalluri

Senior Data Engineer at U.S. Bank building Azure lakehouse pipelines that process 50M+ records daily.

At U.S. Bank, I design Azure data pipelines with Azure Data Factory, Synapse, and Databricks for banking analytics. I revamped production pipelines and data models to support processing 50M+ records daily.

I also engineered PySpark and SQL transformations across Delta Lake and medallion layers, improving curated datasets for Power BI reporting and audit-ready analytics. My work on data lineage and schema design improved data quality metrics by 20%.

At CVS Health, I optimized AWS pipelines and developed on-call protocols and runbooks for a lakehouse platform, improving incident response times by 50%. Earlier at Cognizant, I worked on GCP analytics pipelines and dimensional data models for consulting teams and client reporting.

Experience

Work history, roles, and key accomplishments

Current

Sr. Data Engineer

Current

U.S. Bank

Jun 2025 - Present (1 year 3 months)

Implemented data lineage and schema design strategies that improved data governance, resulting in a 20% increase in data quality metrics and enhanced audit readiness. Revamped production data pipelines and data models to process 50M+ records daily without compromising performance.

Azure Data Factory Synapse Databricks PySpark SQL Delta Lake Power BI Azure DevOps Git Terraform Data Governance Data Lineage Schema Design CI CD

Data Engineer

CVS Health

Jan 2024 - May 2025 (1 year 4 months)

Spearheaded on-call rotation protocols and runbooks for a lakehouse platform, enhancing incident response times by 50%. Orchestrated data quality monitoring and testing, increasing data accuracy by 40% and reducing manual validation time.

AWS Glue S3 Redshift Lambda Airflow Step Functions EMR Kinesis Athena RDS DynamoDB Cloudwatch REST APIs Data Quality Data Modeling Monitoring Incident Response

Associate Data Engineer

Cognizant

May 2021 - Jul 2023 (2 years 2 months)

Optimized dimensional data modeling for production data pipelines, achieving a 30% increase in throughput. Consolidated data quality frameworks and dimensional data modeling to enhance data governance, resulting in a 45% reduction in data discrepancies.