Responsibilities
- Design scalable frameworks for ingesting and transforming data that adapt to shifting schemas and data agreements
- Create and manage ETL and ELT pipelines to process, enhance, and categorize raw data for analytical use
- Work closely with research, hardware, operations, machine learning, data science, and performance teams to validate data capture from firmware, embedded systems, and performance monitoring tools
- Implement automated systems for detecting, versioning, and verifying data schemas to support gradual data model changes
- Uphold high standards for data integrity, including metadata tagging, lineage documentation, and consistency checks
- Support independent data analysis by delivering cleaned datasets, accessible APIs, and interactive Databricks notebooks
Work Arrangement
Hybrid
Other
#LI-Hybrid
