Srikar B
Senior Data Engineer @Orlando Health
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WORK HISTORY
Senior Data Engineer @Orlando Health
FL, US
Designed and deployed scalable ETL pipelines using AWS Glue and PySpark, processing over 50TB/month of structured and semi-structured data for analytics and regulatory reporting.Built real-time streaming ingestion using Kafka and AWS Lambda, reducing event-to-ingestion latency by 70%, enabling faster insights for operations and customer behavior tracking.Automated orchestration of 150+ data pipelines using Apache Airflow, significantly improving scheduling reliability, error handling, and end-to-end data pipeline observability.Developed modular data transformation layers with dbt and PySpark, accelerating dataset delivery timelines by 30% and supporting agile analytics for multiple business teams.Created executive dashboards using Tableau, Redshift, and Athena, providing near real-time visibility into infrastructure utilization, customer churn, and operational KPIs.Conducted deep cost optimization on AWS Glue and EMR clusters, cutting monthly compute spend by $25K without compromising on processing speed or pipeline SLA.Enforced data governance and lineage using Glue Crawlers, Confluence documentation, and AWS Lake Formation, achieving 99% metadata discoverability and full compliance with internal access policies.Implemented CI/CD workflows using Git and Terraform, enabling consistent, version-controlled deployments across dev and prod environments with minimal manual intervention.
EDUCATION
University of Colorado
Masters, Information Systems
ABOUT SRIKAR B
Senior Data Engineer with 9+ years of proven experience designing, building, and scaling enterprise-grade data platforms across AWS, Azure, and GCP. I specialize in architecting robust batch and real-time pipelines using tools like AWS Glue, PySpark, Azure Data Factory, and Databricks delivering actionable insights that power business-critical decisions. At Wells Fargo, I led the migration of legacy pipelines to AWS Lakehouse architecture, reducing processing time by 60% and improving pipeline reliability by 40%. At OPTUM, I developed Kafka-based real-time data ingestion systems supporting over 10 million daily health transactions, enabling near real-time patient analytics.🧠 My expertise spans data lakes (S3, ADLS), orchestration (Airflow, Step Functions), DevOps (CI/CD with Jenkins, Azure DevOps), and secure data governance using IAM, Purview, and Glue Data Catalog. Whether modeling star schemas or implementing scalable CDC pipelines, I bring precision and performance to every solution. I thrive at the intersection of engineering, analytics, and business turning complex data needs into production-ready pipelines that support ML, BI, and compliance.Happy to connect and share ideas on building smart, scalable, and secure data systems.
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