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Tech Stack
Tools & technologiesAWSAzureCloudUnityVault
About the role
Key responsibilities & impact- architecting the framework that empowers our entire organization to make high-stakes, data-driven decisions
- co-own the design of the foundational AI Context Layer
- define the vision, requirements, and roadmap for an Enterprise Ontology and Semantic Layer
- design and deploy semantic abstraction layers to expose canonical data via intelligent APIs
- bridge the gap between physical big data performance and semantic meaning
- lead the design and consolidation of foundational data domains into unified, cleansed Canonical Models
- implement cutting-edge, resilient lakehouse patterns
- partner with ML/DevOps teams to architect and maintain MLOps processes
- develop and enforce enterprise-wide standards for metadata management, data contracts, and semantic version control
- ensure data and its underlying business logic are highly discoverable and machine-readable
- implement reusable data quality and identity resolution frameworks
Requirements
What you’ll need- Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Information Technology or similar major
- 5+ years of experience in data engineering and data architecture in a dedicated Enterprise or Domain Architect capacity
- Proven track record of designing, scaling, and implementing Canonical Models and Semantic Layers that sit on top of massive Enterprise Data Lakes/Lakehouses
- 5+ years of experience with modern cloud data platforms, specifically possessing deep architectural knowledge in at least one of the following: Snowflake (Dynamic Tables, Cortex, Horizon), Databricks (Unity Catalog, Delta Live Tables), or Microsoft Fabric (OneLake, Semantic Models), alongside cloud ecosystems like AWS or Azure
- Advanced skill in Data Modeling across multiple paradigms: Relational, Dimensional (Kimball), and highly relational/flexible frameworks like Data Vault 2.0 or Graph-native concepts
- Demonstrated ability to build, maintain, and version-control an ontology or semantic map (e.g., translating messy source attributes into a single, unified business entity layer)
- Solid understanding of FinOps —managing and optimizing cloud compute/storage consumption to maintain platform ROI while running complex semantic or AI workloads.
Benefits
Comp & perks- career defining opportunities
- thoughtfully curated benefits
- tools to explore and grow
- supported and celebrated environment
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Data EngineeringData ArchitectureCanonical ModelsSemantic LayersData ModelingMLOpsMetadata ManagementData Quality FrameworksIdentity ResolutionCloud Data Platforms
Soft Skills
LeadershipCollaborationProblem-SolvingCommunicationOrganizational Skills