Data Engineer

I am a Data Engineer with 2 years of experience building scalable, production-grade data pipelines and end-to-end data platforms. My core strengths are Python, SQL, PySpark, metadata-driven ELT/ETL design, and Medallion Architecture.

I have worked extensively with Microsoft Fabric, AWS Glue, Azure Data Factory, Databricks, Snowflake, and cloud-based data services. I focus on building reliable Bronze, Silver, and Gold data layers that support analytics, reporting, and operational use cases.

In my current role, I have built an AWS Glue platform that processes 1.2 TB of MongoDB data into Amazon S3 and Aurora PostgreSQL. I have also implemented secure infrastructure, audit tracking, monitoring, SLA alerts, and incremental-loading optimizations.

I have experience developing metadata-driven Microsoft Fabric ELT frameworks using modular PySpark notebooks and master-child pipeline orchestration. I have migrated more than 80 Azure Data Factory pipelines to Fabric-native components while using reconciliation testing and rollback planning to protect production workloads.

I care deeply about data quality, observability, scalability, and cost-efficient processing. I have also built a RAG-based business data assistant that enables non-technical users to query databases in plain English and receive results or visualizations.