Naveen Sai Reddy Vengalathur
@naveensaireddyvengal
Data engineer specializing in scalable cloud data pipelines, real-time streaming, and ML operationalization.
What I'm looking for
I am a data engineer with 5+ years building scalable ETL and streaming solutions across Azure and GCP, focused on healthcare and finance domains. I design and optimize data warehouses, automate CI/CD and infrastructure with Terraform and Azure DevOps, and operationalize ML models with MLflow and TensorFlow.
At CVS I improved ETL efficiency by 30%, reduced streaming latency by 40%, and ensured 99.9% data accuracy through automated validation and Databricks workflows. At Infosys I reduced batch processing time by 40%, improved Snowflake query performance by 25%, and implemented real-time ingestion patterns with Snowflake Streams and GCP Dataflow.
I prioritize data governance, security, and performance—using tools like Azure Purview, Dynamic Data Masking, and RBAC—to deliver reliable, compliant analytics platforms that accelerate business insights.
Experience
Work history, roles, and key accomplishments
Data Engineer
CVS
Jan 2024 - Present (2 years)
Designed and maintained scalable ETL pipelines on Databricks, improving ETL efficiency by 30% and reducing streaming data latency by 40% to support pharmacy and insurance analytics while ensuring 99.9% data accuracy for HIPAA compliance.
Data Engineer
Infosys
Dec 2019 - Nov 2022 (2 years 11 months)
Developed and optimized big data pipelines on Google Dataproc and Snowflake, reducing batch processing time by 40% and query execution times by 25% while implementing real-time ingestion and data governance controls.
Education
Degrees, certifications, and relevant coursework
The University of Texas at Arlington
Master of Science, Business Analytics
Completed a Master of Science in Business Analytics with coursework and projects focused on data engineering, cloud analytics, and machine learning.
Vellore Institute of Technology
Bachelor of Technology, Technology
Earned a Bachelor of Technology with emphasis on engineering fundamentals and data-related coursework supporting subsequent roles in data engineering.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Job categories
Skills
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