Sindhura K
Senior E2E Analytics Analyst at Kenvue
- Role
- Sr E2e Data Scientist at Kenvue
- Location
- Durham, NC, US
- LinkedIn followers
- 500 followers
About Sindhura K
Data Engineering professional with almost 4 years of experience implementing, leading…
Experience
Sr E2e Data Scientist
Jun 2023 — Present · NJ, US
Engineered Azure Data Factory pipelines to orchestrate complex data workflows on inventory and material management data from multiple sources (SAP, Oracle, Databricks), consolidating data to Snowflake, resulted 400-hour annual manual workload reduction. Leadership of cross-functional teams to address workflow complexities and resolve data model discrepancies in Databricks, implementing automated reporting processes that saved 12+ hours weekly across teams and significantly increased operational efficiency. Migration of pipelines to Kenvue’s Data Engineering platform, optimizing processes and addressing pre-existing gaps for a 20% increase in operational effectiveness, contributing to the successful TSA exit by Q3 2024 as part of the corporate separation from J&J Development of lane-level predictive cost models using Random Forest and clustering techniques with minimal MAPE %, generating cost-saving recommendations through close collaboration with project teams and data scientists Transition of legacy Alteryx workflows to Databricks, enhancing scalability, data processing efficiency, and flexibility to handle larger datasets and more complex transformations Improved Databricks cluster performance and cost efficiency through performance tuning, cluster metric analysis, and implementing caching strategies and data partitioning achieving a 40% increase in efficiency and a 15% reduction in operational costs Proactively monitored expiration schedules for Databricks secrets and scopes, implemented manual refresh protocols for credential renewals, resulting in enhanced cluster stability, minimized downtime, and improved security compliance. Developing a fully automated mentorship algorithm using Pandas, with custom UDFs to create mix-and-match mappings that enable cross-functional collaborative learning across the organization.
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