Build and maintain data pipelines, transformations, semantic models, and curated datasets across engineering and operational data sources. Partner with product management, engineering teams, support teams, and leadership stakeholders to define metrics, key business measures, and data quality expectations. Develop telemetry validation, data freshness, completeness, and reliability checks. Integrate data from systems such as Azure DevOps, telemetry platforms, support and escalation systems, release-readiness sources, and business reporting tools. Automate manual reporting and intake workflows where possible. Improve discoverability, governance, ownership, and documentation of critical datasets. Support release readiness, customer health, feature adoption, supportability, and escalation analytics. Investigate data issues, root-cause reporting anomalies, and drive fixes with upstream and downstream partners. Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 2+ years experience in business analytics, data science, software development, data modeling, or data engineering. OR Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 1+ year(s) experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience. Experience with Python and PySpark scripting for data transformation, automation, validation, or analytics workflows. Experience building extract-transform-load or extract-load-transform pipelines, scheduled data refreshes, production data workflows, or reporting data models. Experience with telemetry, observability, product usage, supportability, or reliability datasets. 4+ years of experience with large-scale data platforms, telemetry, business intelligence reporting, or operational analytics. Proven experience developing and deploying autonomous artificial intelligence agents or agentic automation systems that reason over data, operate reliably in production, and help redefine data engineering through continuous, automated insight generation.