Principal Data Analytics Engineer

Recruitment Fraud Alert

We’ve learned that scammers are impersonating Commvault team members—including HR and leadership—via email or text. These bad actors may conduct fake interviews and ask for personal information, such as your social security number.

• Commvault does not conduct interviews by email or text.

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If you suspect a recruiting scam, please contact us at wwrecruitingteam@commvault.com

Commvault (NASDAQ: CVLT) is the gold standard in cyber resilience. The company empowers customers to uncover, take action, and rapidly recover from cyberattacks – keeping data safe and businesses resilient. The company’s unique AI-powered platform combines best-in-class data protection, exceptional data security, advanced data intelligence, and lightning-fast recovery across any workload or cloud at the lowest TCO. For over 25 years, more than 100,000 organizations and a vast partner ecosystem have relied on Commvault to reduce risks, improve governance, and do more with data.

The Principal AI & Semantic Engineer is the senior technical authority responsible for connecting governed enterprise data and business meaning with scalable analytics and artificial intelligence solutions. This role defines the architecture, standards, and engineering patterns for semantic models, governed metrics, AI-ready data products, machine learning operations, and generative AI consumption.

Operating at the intersection of Analytics, Data Engineering, Data Science, Governance, and AI Platform Engineering, this role ensures that enterprise data, metrics, models, and knowledge assets can be reliably discovered, understood, deployed, monitored, and reused. The Principal AI & Semantic Engineer provides hands-on technical leadership, guides complex implementations, and mentors engineers and analysts while remaining an individual contributor.

What you’ll do…

Semantic Architecture & Governed Metrics

• Define enterprise standards and reference patterns for semantic models, dimensional models, business metrics, KPIs, hierarchies, calculations, and reusable analytical assets.

• Architect semantic consumption patterns across Microsoft Fabric, Power BI, Databricks Gold-layer data, and approved AI applications.

• Partner with business domain owners, analysts, and Data Governance to translate business concepts into consistent, governed definitions and analytical models.

• Lead design reviews for semantic models and analytical data products, balancing usability, reuse, performance, security, and maintainability.

• Ensure semantic assets are documented with appropriate metadata, lineage, ownership, and change controls.

AI Engineering & MLOps

• Define engineering standards for deploying, versioning, testing, monitoring, and governing machine learning and generative AI solutions.

• Architect reusable MLand LLM patterns, including CI/CD, model and prompt versioning, experiment tracking, evaluation, observability, rollback, and production support.

• Guide implementation of feature pipelines, model registries, inference workflows,