Principal Data Analytics Engineer

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About Commvault

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 companys 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 100000 organizations and a vast partner ecosystem have relied on Commvault to reduce risks improve governance and do more with data.

The Opportunity:

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 youll 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 retrieval pipelines vectorized knowledge assets and AI evaluation frameworks.
• Partner with Data Scientists and AI product teams to move prototypes into secure scalable and supportable production services.
• Implement technical controls for traceability reproducibility model performance data drift responsible AI requirements and operational reliability.

AI-Ready Data & Knowledge Enablement

• Design curated governed data products and knowledge assets for predictive analytics generative AI copilots agents and retrieval-augmented generation.
• Define how structured data enterprise metrics metadata documents embeddings and retrieval indexes are linked and consumed.
• Ensure AI applications use approved business definitions metrics access policies and authoritative data sources.
• Collaborate with Architecture and Governance teams on ontology glossary metadata lineage classification and policy integration.

Technical Leadership & Collaboration

• Serve as the technical escalation point for complex semantic analytics MLOps and AI engineering decisions.
• Create reference implementations reusable frameworks coding standards and engineering playbooks.
• Mentor Senior Analytics & AI Engineers Semantic Engineers Data Analysts and Data Scientists.
• Influence cross-functional roadmaps and communicate architecture decisions trade-offs risks and business outcomes to technical and executive audiences.

Evaluate emerging technologies and recommend pragmatic adoption paths aligned with enterprise architecture and governance standards.

Who you are

• 10 years of experience across analytics engineering business intelligence semantic modeling data engineering machine learning engineering MLOps or related disciplines.
• Demonstrated experience serving as a principal-level technical leader or architect for enterprise data analytics or AI solutions.
• Deep expertise in SQL dimensional modeling semantic model design analytical performance optimization and governed metric implementation.
• Hands-on experience with Microsoft Power BI and/or Microsoft Fabric semantic models including DAX and enterprise-scale model design.
• Hands-on experience with Databricks or a comparable cloud data and AI platform including production data pipelines and model lifecycle capabilities.
• Experience designing or operating CI/CD experiment tracking model registries monitoring evaluation and observability for ML or generative AI solutions.
• Working knowledge of generative AI patterns such as embeddings vector retrieval RAG prompt and model evaluation and agent-based solutions.
• Strong understanding of metadata lineage data quality access control business glossary and responsible AI principles.
• Proven ability to mentor technical professionals lead architecture reviews and influence stakeholders without direct authority.
• Bachelors degree in Computer Science Engineering Data Science Information Systems or a related field; equivalent practical experience will be considered.

Preferred Qualifications

• Experience with Microsoft Purview Unity Catalog MLflow Azure DevOps or GitHub Actions and cloud observability tooling.
• Experience supporting enterprise Finance GTM Product Customer or Workforce analytics domains.
• Experience establishing reusable semantic or AI platform capabilities in a federated operating model.

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