Shreya Ghatnatti

Built and maintained 20+ production-grade data pipelines using Azure Data Factory and SQL, enabling real-time ingestion of server and workstation data for vulnerability analytics. Leveraged Databricks to process large-scale datasets, reducing pipeline execution time by 40% and increasing data throughput efficiency. Consolidated multiple structured sources into a unified Azure Data Lake schema, improving data accuracy and reducing reporting discrepancies by 30%. Implemented logic-based scheduling and data quality checks, ensuring 100% SLA adherence and delivery of analytics-ready data across teams. Facilitated cross-functional collaboration with product and engineering teams to align pipelines with evolving business logic, accelerating data-driven decisions by 15%. Standardized pipeline architecture and reusable components, resulting in a 20% boost in engineering efficiency and system maintainability.

Designed and managed 40+ scalable pipelines using Apache Spark and Azure Data Lake Storage, powering month-end ledger processing across AP, AR, and Inventory. Unified data from multiple structured systems into a centralized cloud repository, improving accessibility and report generation times. Led full-scale migration from on-prem to Azure cloud, reducing retrieval latency by 45% and optimizing cost and performance. Cut month-end closure time from 710 days to just 12 hours (CD-1), and reduced compute costs by 25% through refined pipeline orchestration. Developed a Data Veracity Framework to validate schema and row-level consistency across ingestion layers, improving data trust by 30% and supporting audit readiness.

Analyzed user behavior patterns and feedback data to deliver insights that shaped product improvements and informed future feature prioritization. Collaborated with data, product, and engineering teams to bridge gaps between user needs and technical implementation, ensuring alignment between product outcomes and market feedback.