Principal Data Architect

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 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 Opportunity

The Principal Data Architect is a senior, hands‑on architecture role responsible for defining and driving Commvault's enterprise data architecture strategy. This role establishes the vision, standards, and roadmap for how data is modeled, integrated, governed, and delivered across the organization to support scalable analytics, operational reporting, and AI/ML use cases. This role combines deep, practical expertise in modern cloud data platforms (e.g., Snowflake, Databricks, Azure) with strong architectural leadership. The Principal Data Architect defines architecture principles and standards, influences platform and tooling decisions, and partners with data engineering and cross‑functional teams to ensure solutions are aligned to enterprise data strategy, governance frameworks, and long‑term scalability. What You’ll Do

Enterprise Data Architecture & Design

Define and maintain the enterprise data architecture, including conceptual, logical, and physical data models across key domains (e.g., sales, finance, product, customer, operations). Design data models (e.g., dimensional, data vault, 3NF) and integration patterns that support analytical, operational, and self‑service BI use cases. Create and socialize data architecture standards and patterns, including naming conventions, modeling guidelines, and design best practices. Ensure data solutions are designed for scalability, performance, reliability, and cost‑efficiency on the target cloud platforms. Partner with data engineering to convert architecture designs into robust, production‑grade data pipelines and structures. Data Platform & Integration

Provide senior architectural leadership for Commvault's cloud data platforms, including Snowflake, Databricks, and Azure data services (e.g., Azure Data Lake, Azure SQL, Synapse). Define and govern end‑to‑end data flows across source applications, APIs, streaming, and batch ETL/ELT processes, including medallion/lakehouse patterns. Design and maintain reference architectures for ingestion, curation, serving, semantic, and consumption layers, ensuring alignment with BI, analytics, and AI/ML needs. Work with infrastructure, security, and platform teams to ensure data platforms meet availability, resilience, observability, and security requirements. Influence and evaluate technology choices, tools, and vendors related to the data platform and integration ecosystem. Promote a unified semantic layer that serves both BI tools and AI systems, ensuring consistent definitions, metrics, and governed access across analytics and AI use cases. Data Governance, Quality & Security

Collaborate with data governance to embed policies and standards (e.g., data ownership, classification, retention, privacy) directly into platform and model designs. Ensure the architecture supports data quality, lineage, metadata management, and cataloging, leveraging tools such as Microsoft Purview or similar. Define and oversee access control and security models (e.g., role‑based, domain‑based, row/column‑level security) consistent with compliance and audit requirements. Provide architectural leadership for master data management (MDM), reference data, and golden record strategies across domains. AI/ML & Advanced Analytics Enablement

Define architectural patterns to enable secure, governed access to enterprise data for AI systems, including support for retrieval‑augmented generation (RAG), semantic layers, and API‑based data access abstractions. Define and standardize integration patterns for Model Context Protocol (MCP) and similar tool‑based interfaces, enabling AI agents and copilots to interact with enterprise data, APIs, and services in a governed and auditable way. Partner with data science, analytics,

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