Mansi Kashyap

Built scalable Python-based ETL and alerting pipelines, ingesting high-volume security telemetry from SentinelOne, CrowdStrike, and Microsoft Defender, improving data availability and SLA compliance by ~30%. Standardized incident schemas & reusable data pipeline frameworks, reducing manual alert triage by ~35% and improving MTTR by ~ 45% in production environments. Delivered end-to-end, client-facing analytics data pipelines translating complex security datasets and business requirements into scalable, production-grade data solutions. Contributed to GCP cloud data pipeline migration, Cribl Stream and post-migration pipeline health checks, achieving ~40% operational overhead reduction.

Delivered a client-facing AWS data integration project, leading a 5-member team across requirements, execution, and ETL production deployment, ensuring on-time delivery. Built PySpark (Apache Spark) ETL transformations using AWS Glue to process ingestion datasets into analytics-ready warehouse tables. Designed a S3 Data Lake architecture and implemented metadata governance using AWS Glue, improving schema management and dataset discoverability across analytics pipelines. Performed SQL-based data transformations and validation using AWS Athena, optimizing transformation logic and data models to improve reporting turnaround by ~25% and reduce validation effort by ~20%. Improved delivery efficiency by ~15% through stakeholder alignment while mentoring an intern and integrating near real-time ingestion modules into production ETL pipelines.

Automated marketing data pipelines using Google Analytics and Facebook Ads APIs, enabling real-time campaign tracking. Developed Python-based ETL pipelines for data migration and automated reporting, reducing manual effort by ~65%. Partnered with marketing teams using data analytics insights to optimize ad targeting driving ~50% uplift in traffic and sales. Developed automated sales reporting & product-level margin analysis enabling data-driven pricing and inventory decisions.