Ramya Anugu

I’m a Data Engineer with over 5+ years of hands-on expertise in building data engineering solutions end-to-end—from ETL (Extract, Transform, Load) to data warehousing, data processing, and transformation. I focus on designing and implementing data pipelines that meet business requirements with data accuracy, availability, security, and performance.

In my current role as an Azure Data Engineer at Microsoft (Canada), I developed and orchestrated scalable data pipelines using Azure Data Factory to integrate multiple data sources for analytics. I designed and optimized enterprise-grade data warehouse solutions on Snowflake, including schema modeling, data loading strategies, and query performance tuning.

I also implement proactive monitoring and reliability practices using Azure Monitor, setting up monitoring and alerting to identify pipeline failures and reduce downtime. To strengthen governance and security, I use Azure Key Vault for secure management of secrets, keys, and certificates, and I optimize NoSQL database performance in Azure Cosmos DB with partitioning, indexing policies, and throughput tuning.

Beyond batch workloads, I build real-time streaming pipelines with Azure Event Hubs and Stream Analytics for low-latency, actionable insights. I’ve also extended my platform work with Azure Synapse Analytics and Microsoft Fabric (Lakehouse architecture) and develop PySpark- and Python-/SQL-based automation for data validation, transformation, and deployment workflows.