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The Lead Data Architect is a senior member of the Data team responsible for designing, developing, and maintaining enterprise and system-level conceptual, logical, and physical data models using Data Vault methodology within a Data Mesh architecture. This role translates the organization's data product strategy into actionable architecture solutions, ensuring data models, standards, and domain-aligned data products are consistently delivered across the enterprise. The Lead Data Architect works closely with data domain owners, Data Engineers, Analytics teams, and business stakeholders to build and evolve scalable, well-governed data products on the organization's Databricks-based platform, leveraging a custom automation framework and modern lakehouse patterns to drive domain-oriented, self-serve data capabilities.

   Lead the design and development of conceptual, logical, and physical data models using Data Vault methodology for the enterprise Databricks lakehouse platform, ensuring alignment with architectural standards and business requirements
   Architect and evolve domain-aligned data products within a Data Mesh framework, ensuring each data domain delivers discoverable, addressable, trustworthy, and self-describing data products
   Collaborate with data domain owners, Data Engineering, Analytics, and Business Relationship Management (BRM) teams to translate business needs into robust, domain-oriented data architecture solutions
   Define and maintain data modeling standards, naming conventions, and design patterns that support Data Vault modeling, Data Mesh principles, and a consistent data product lifecycle across the enterprise
   Design and extend the organization's custom automation framework on Databricks, leveraging latest platform components (Unity Catalog, Delta Live Tables, Databricks Workflows, Databricks Asset Bundles) to accelerate data product delivery
   Develop and enforce a decision-making framework aligned with data domains and data products, enabling domain teams to make autonomous architecture decisions within guardrails
   Drive the implementation of master data management (MDM), metadata management, and data governance initiatives, leveraging Unity Catalog and platform-native capabilities for federated governance
   Conduct data architecture reviews for projects and data products to ensure compliance with enterprise data standards, Data Mesh principles, policies, and security requirements
   Collaborate with the CISO and data protection teams to ensure data classifications, data zoning, and security requirements are embedded into data product and platform designs
   Create and maintain architecture documentation, including data product specifications, data flow diagrams, entity-relationship diagrams, data dictionaries, and domain boundary definitions
   Support the development of enterprise data governance processes and structures, including data quality frameworks, data contracts, and data product SLAs
   Identify and resolve data architecture issues, including data redundancy, cross-domain integration challenges, and performance bottlenecks across the Databricks platform
   Participate in strategic roadmap planning by providing technical assessments and architecture recommendations for data products and analytics initiatives
   Evaluate emerging Databricks features, partner integrations, and data management trends to recommend improvements to the platform and automation framework
   Mentor and provide technical guidance to Data Engineers and junior team members on Data Vault modeling, Data Mesh practices, and platform best practices
   Facilitate communication with Databricks and other technology partners for technical evaluation, proof-of-concept efforts, and troubleshooting
   Balance tactical project delivery with adherence to long-term data product strategy and enterprise architectural vision
   Be accountable and responsible for the accuracy and completeness of assigned work and results
   Prioritize and manage workload and communicate issues clearly
   Complete other tasks as required
   Travel when required

   Bachelor's degree from an accredited institution in Computer Science, Information Systems, Data Management, or a related field, or equivalent industry experience

   8+ years of experience in Information Technology, with a majority of experience in Data Architecture or Data Engineering
   5+ years of experience designing, building, and implementing enterprise data platforms, preferably on Databricks or similar lakehouse platforms
   3+ years of hands-on experience with Data Vault modeling methodology
   Experience applying Data Mesh principles, including domain-oriented data ownership and data product thinking
   Experience building or contributing to automation frameworks for data platform operations
   Experience leading data architecture efforts across multiple concurrent projects or workstreams
   Experience mentoring or providing technical guidance to team members

   Strong experience using data architecture and modeling tools such as Archimate, ER/Studio, or Erwin
   Strong experience in Data Vault 2.0 methodology, including hubs, links, satellites, and point-in-time/bridge table patterns
   Solid understanding of Data Mesh architecture principles, including domain ownership, data as a product, self-serve data platform, and federated computational governance
   Strong experience defining and delivering data products with clearly defined contracts, SLAs, and quality standards
   Strong hands-on experience with the Databricks platform, including Unity Catalog, Delta Live Tables (DLT), Databricks Workflows, Delta Lake, Databricks Asset Bundles, and Databricks SQL
   Experience building or extending custom automation frameworks for data pipeline orchestration, deployment, and platform operations on Databricks
   Solid experience in master data management (MDM), data governance, and metadata management practices, including federated governance using Unity Catalog
   Strong knowledge of Azure cloud services that complement Databricks, including Azure Data Factory, Azure Analysis Services, and Power BI
   Strong expertise in enterprise-level data modeling, including relational, dimensional, and NoSQL design patterns
   Understanding of metamodels, taxonomies, and ontologies, and the ability to apply structured modeling techniques to semi-structured and unstructured data sources
   Experience with decision-making frameworks that align architecture choices with data domain boundaries and data product ownership
   Familiarity with data science concepts, business intelligence, and data warehouse design and implementation techniques
   Experience with distributed data management and analytics in cloud and hybrid environments, including microservices and event-based architectures
   Knowledge of problem analysis, structured analysis and design, and programming techniques (Python, SQL, Spark)
   Ability to assess emerging Databricks features and partner ecosystem technologies and evaluate their applicability to business needs
   Strong business acumen with the ability to communicate effectively with data domain owners, business stakeholders, and technical staff
   Experience with innovation and ideation methods such as design thinking
   Business domain understanding; experience with Operations, Finance, Sales, or Marketing is preferred
   Strong experience working in Agile/SCRUM, KANBAN, and ITSM teams
   Ability to mentor and guide team members on Data Vault modeling, Data Mesh practices, and platform best practices
   Strong written and verbal communication skills
   Strong organizational skills, including the ability to manage multiple priorities and workstreams
   Proven work ethic with the utmost integrity
   Self-awareness, with a desire for constant self-improvement (goal-oriented)
   Ability to build positive partnerships and work collaboratively with cross-functional and cross-domain teams

The physical demands and work environment described here are representative of those that must be met and/or encountered by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to use hands to finger, handle, or feel; reach with hands and arms; and communicate. The employee may be required to stand, walk, and sit.
Specific vision abilities required by this job include the ability to view monitors, technical documents, and reference material.
The noise level in the work environment is usually low to moderate.