Principal Data Analytics Engineer
Commvault (company site, opens in a new tab)10+ yrsBengaluruOn-siteDirect career pageEst. 35–60 LPA
Posted 3h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at Commvault
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Commvault you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Define and govern enterprise semantic models, metrics, and analytical data products, ensuring usability, performance, and security across platforms like Microsoft Fabric and Power BI.
- Architect and standardize MLOps and AI engineering patterns for machine learning and generative AI lifecycle management, including CI/CD, monitoring, and responsible AI compliance.
- Provide hands-on technical leadership, mentor engineers and analysts, and oversee architecture decisions impacting data, analytics, and AI platforms at the enterprise level.
Minimum Requirements
- 10+ years of experience in analytics engineering, BI, semantic modeling, data engineering, machine learning engineering, or related fields.
- Proven senior technical leadership or architect role experience for enterprise data, analytics, or AI solutions.
- Proficient in SQL, dimensional and semantic modeling, Microsoft Power BI (including DAX), and Databricks or similar cloud AI platforms.
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or equivalent practical experience.
Ideal Candidate Profile
- Experienced in federated data and AI platform operations integrating Analytics, Data Engineering, Data Science, and Governance.
- Strong skills in enterprise-grade MLOps and generative AI engineering, including knowledge of embeddings, vector retrieval, and RAG techniques.
- Capable of influencing cross-functional roadmaps and communicating complex technical decisions to both technical and executive stakeholders without direct authority.