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
What to know: Commvault does not conduct interviews by email or text
We will never ask you to submit sensitive documents (including banking information, SSN, etc) before your first day
If you suspect a recruiting scam, please contact us at wwrecruitingteam@commvault.com 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 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
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 ta