Shruti Sinha

Built a scalable Azure data platform for sales analytics and AI use cases. Developed ETL/ELT pipelines using Azure Data Factory together with Azure storage and Databricks, implementing processing with PySpark, Python, and SQL. Integrated generative AI capabilities, including RAG and LLM/agentic workflows, into enterprise-facing applications.

Automated business processes including reconciliation, validation, and reporting using Python, SQL, and VBA. Built Power BI and Tableau dashboards for KPI monitoring and trend analysis to support business reporting. Performed data validation and root-cause analysis to improve data accuracy and operational consistency.

Performed SQL and Cognos-based validation and analysis of customer data to ensure accuracy, consistency, and compliance. Worked with business analysts and technical teams to translate requirements into data solutions. Helped resolve complex data-related issues by investigating and validating underlying datasets.

Worked on supply chain management reporting by consolidating data from multiple SQL Server databases. Developed and optimized SQL procedures, triggers, queries, and data models to support business intelligence and downstream analytics. Resolved production database issues and improved reliability and efficiency of SQL scripts for ongoing processing.

Designed and built a RAG-based customer support chatbot pipeline using document processing, chunking, embeddings, and vector database retrieval. Implemented semantic search and LLM-based response generation for chatbot answers. Added data quality validation checks and used logs/monitoring to perform root-cause analysis and corrective actions, with CI/CD practices using Git/GitHub.