Principal Data Architect

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

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.

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)

 

Ready to #makeyourmark at Commvault? Apply now! 

 

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