Principal Data Architect

Principal Data Architect

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, and product teams to design data architectures that enable AI and advanced analytics, including feature-ready datasets and governed data products.
• Define patterns for real-time and near-real-time integrations, event streams, and APIs that support AI/ML use cases and intelligent applications.
• Ensure data architectures can support feature stores, experimentation, model monitoring, and explainability requirements.

Stakeholder Partnership & Solution Delivery

• Act as a trusted advisor to IT leadership and business stakeholders, helping shape roadmaps and solution options that align with the target data architecture.
• Lead and contribute to intake, estimation, and solution design for major initiatives, clearly articulating trade-offs, risks, and architectural decisions.
• Provide design authority across projects, ensuring solution teams adhere to standards while balancing pragmatism and delivery timelines.
• Collaborate with product owners, project managers, and engineering leads to ensure consistent alignment to the target data architecture and platform strategy.

Leadership, Enablement & Continuous Improvement

• Provide technical leadership and mentorship to data engineers, data modelers, and analysts.
• Lead and contribute to architecture review boards, design sessions, and governance forums focused on data and analytics.
• Develop and maintain architecture documentation, including diagrams, standards, and design templates.
• Stay current with emerging technologies, architectural patterns, and best practices in data platforms, analytics, and AI/ML—and make recommendations for adoption where appropriate.

Who You Are...

Essential Skills & Experience

• Bachelor's degree in Computer Science, Information Systems, Data Management, or related field; or equivalent practical experience.
• 10+ years of experience in data engineering, database development, analytics, or related roles, with at least 5+ years focused on data architecture or solution architecture for data platforms.
• Deep hands-on experience with cloud data platforms, including Snowflake, Databricks, and Azure data services (e.g., Azure Data Lake, Azure SQL, Synapse), and modern data stack components.
• Proven expertise in data modeling (dimensional, normalized, and/or data vault) and designing enterprise-scale data warehouses, data marts, or Lakehouse-style architectures.
• Strong hands-on experience with ETL/ELT tools and frameworks (e.g., dbt, Azure Data Factory, Spark, or similar) and associated CI/CD practices.
• Solid understanding of data governance, data quality, metadata management, security, and privacy concepts and how to operationalize them in architecture.
• Demonstrated ability to lead architecture for complex, cross-functional initiatives, influence decisions, and communicate effectively with both technical and non-technical stakeholders.
• Excellent analytical, documentation, and communication skills, with the ability to simplify complexity and drive shared understanding
• Certifications: TOGAF (required)

Preferred Skills & Experience

• Experience working in or supporting SaaS, B2B software, or enterprise technology organizations.
• Familiarity with BI/analytics tools such as Power BI, Tableau, or similar, including semantic models and data access patterns.
• Exposure to data catalog and governance tools (e.g., Microsoft Purview, Collibra, Alation, or similar).
• Experience with MDM and reference data management across multiple business domains.
• Knowledge of AI/ML data needs and designing data architectures that support feature stores, experimentation, and model monitoring.
• Background working in Agile or product-oriented delivery environments.
• Certifications: Databricks Azure Platform Architect or Databricks Data Engineer Professional

You'll love working here because...

• Continuous professional development, product training, and career pathing
• An inclusive company culture, opportunity to join our Employee Groups
• Generous benefits supporting your health, financial security, and work-life balance
• Employee stock purchase plan (ESPP)