Results-driven Data Engineer with 4+ years of experience designing and optimizing cloud-native data platforms across Azure, AWS, and Snowflake ecosystems. I build scalable ETL/ELT pipelines, real-time streaming architectures, and data lakehouse solutions that power enterprise analytics and AI/ML initiatives.

• Designed enterprise-scale data platforms on Azure Synapse, Databricks & Snowflake
• Built 25+ ETL/ELT pipelines integrating structured & semi-structured data
• Implemented Kafka streaming pipelines reducing latency by 50%+
• Created reusable DBT transformation frameworks across projects
• Automated infra with Terraform, Docker & Kubernetes
• Implemented data quality with Great Expectations

• Engineered pipelines on AWS S3, Glue, Redshift & Snowflake
• Built distributed Spark / PySpark workloads for large-scale analytics
• Designed Kafka streaming architectures for real-time ingestion
• Migrated legacy ETL workflows to cloud-native architectures
• Reduced SQL/Spark processing times by ~35%
• Automated CI/CD with GitHub Actions and Terraform

Cloud-native lakehouse using Azure Databricks + ADLS + DBT + Snowflake

• Implements Medallion Architecture (Bronze / Silver / Gold layers)
• Automated ingestion, transformation & reporting pipelines
• Data validation via Great Expectations
• Databricks DBT Snowflake ADLS Great Expectations

Kafka + Spark Streaming for high-volume near real-time event processing

• Datadog monitoring & automated alerting
• High availability for mission-critical workloads
• Kafka Spark Streaming Datadog Prometheus

M.S. Information Technology — University of Cincinnati · December 2024

• 📧 saigolla0320@gmail.com
• 📞 +1 (216) 302-8283
• 💼 LinkedIn

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