Praveen Veerabattini
Azure Data Engineer @One United Bank
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WORK HISTORY
Azure Data Engineer @One United Bank
MA, US
Led end-to-end migration of 500+ TB of legacy banking datasets — transactions, loan records, and KYC data — from on-premises to AWS Cloud, reducing data latency by 65% using AWS Glue, EMR (Spark), AWS DMS, and Kinesis Data Streams for CDC-based near real-time pipelines. Built an ACID-compliant Delta Lake architecture on S3 with Lake Formation, Glue Data Catalog, and Informatica, and developed modular dbt transformation models with lineage tracking and validation — boosting BI reporting freshness by 50% while meeting GDPR and PCI-DSS compliance through IAM RBAC, AWS KMS encryption, and VPC security policies.Engineered ML-based anomaly detection pipelines using TensorFlow and Hugging Face within Apache Airflow (MWAA), surfacing insights via Tableau dashboards, while ensuring data quality using Great Expectations — resolving 55% of inconsistencies pre-cutover. Deployed 50+ digital banking services with zero downtime via blue-green strategy on Amazon EKS and Docker, and built resilient CI/CD pipelines using GitHub Actions, Jenkins, and AWS CodePipeline, improving pipeline reliability by 45% with full observability through CloudWatch and CloudTrail.
EDUCATION
Osmania University, Hyderabad
Bachelor's Degree, Computer Science
ABOUT PRAVEEN VEERABATTINI
Data Engineer with 6 years of hands-on experience architecting and scaling enterprise-grade data platforms across the healthcare and finance industries. Specializing in building robust data lakehouses and warehouses on AWS (S3, Glue, EMR, Redshift), Apache Spark, Snowflake, and Hadoop, with deep proficiency in orchestration tools like Apache Airflow and Kafka-driven streaming pipelines. Proven track record of leading large-scale implementations processing 25M+ daily records and 200TB+ of historical data, delivering up to 35% improvement in ETL efficiency through optimized ingestion strategies, schema design, and distributed computing frameworks. Adept at dimensional modeling, SCD Type 2, data vault patterns, and advanced performance tuning techniques including indexing, partitioning, clustering, and materialized views across Snowflake, Oracle, and SQL Server — driving query performance improvements of up to 40%. Experienced in transformation layer engineering using dbt, alongside Python, PySpark, and SQL, to deliver clean, scalable, and audit-ready data models that power risk analytics, regulatory reporting, and executive dashboards. Brings deep domain expertise in healthcare analytics — including Medicare Advantage, RAF scoring, HEDIS metrics, CMS compliance, and HIPAA-compliant data governance — as well as financial risk and fraud analytics pipelines. Thrives in Agile delivery environments, leveraging CI/CD pipelines, automated data quality frameworks (Great Expectations, dbt tests), metadata management, and workload monitoring tools like Grafana and CloudWatch to ensure SLA adherence, pipeline observability, and enterprise-grade reliability at scale. Feel free to message me or email me at – I’m always excited and actively looking to discuss opportunities and collaborate!
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