At Tiger Analytics, I own data engineering workstreams spanning ingestion, data quality, transformation, and platform migration, delivering Bronze and Silver datasets for downstream analytics.
I've built and maintained end-to-end Azure Data Factory pipelines, improved paginated API ingestion for sources including SFMC, and implemented historical and incremental loads for datasets such as Chewy. I also perform DDL preparation, validation, and data-quality checks across multiple datasets.
As a core contributor to the MDIF ingestion and orchestration framework, I've deployed more than 10 datasets and developed eight production connectors, including Walmart Item 360, Stackline, and Amazon Seller API. My reusable development guidance has been adopted by more than eight engineers.
I build practical, reusable data-platform solutions, including a generalized reconciliation module and Databricks Genie prompts that reduced development effort by about eight hours per dataset. I also created DeltaFlow, an Azure lakehouse project using Azure Data Factory, Databricks, PySpark, SQL, and Power BI.
