Senior Data Analytics Engineer

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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 Senior Data Analytics Engineer is responsible for designing, developing, and operating governed analytics, semantic models, reusable business metrics, and AI-ready analytical assets. This role combines analytics engineering, semantic modeling, business intelligence, data quality, and practical AI enablement to deliver trusted data products that support reporting, decision-making, self-service analytics, and approved AI use cases.

The position partners closely with Data Engineering, Data Governance, Data Science, business analysts, and application teams to translate business definitions, source-system context, and analytical requirements into scalable semantic models and governed consumption layers. The ideal candidate is a hands-on senior individual contributor with deep SQL, BI, semantic modeling, and analytics engineering expertise, along with working knowledge of data science, knowledge graphs, retrieval-augmented generation, and AI-ready data patterns.

What you’ll do…

Analytics Engineering, Semantic Models & Data Products

• Design, build, test, and maintain semantic models, dimensional models, curated datasets, measures, KPIs, hierarchies, and reusable business logic.

• Develop enterprise analytics solutions using SQL, Power BI, Microsoft Fabric, Databricks, and approved cloud services.

• Consume governed Gold-layer data and work with Data Engineers to resolve modeling, quality, performance, and integration issues.

• Optimize semantic models and analytical queries for usability, scalability, refresh performance, and secure access.

• Support dashboard developers, analysts, and self-service users with well-documented analytical assets.

Applied Data Science & AI Readiness

• Apply working knowledge of statistical methods, machine learning concepts, and AI patterns to design analytics assets that can support downstream data science and AI use cases.

• Partner with AI and Engineering teams to understand modeling, feature, evaluation, and retrieval requirements and translate them into reliable analytical datasets and reusable data products.

• Develop curated feature-ready datasets, dimensional models, and semantic structures that support forecasting, segmentation, classification, anomaly detection, and other approved analytical use cases.

• Support generative AI and RAG solutions by preparing high-quality business definitions, metadata, retrieval-ready