Enterprise Data Architect
Core
Define and evolve enterprise data architecture principles, standards, and build specifications for a global gaming content and technology company.
Role type
Senior Enterprise Data Architect
Builds
Enterprise data ecosystem, canonical data models, and interoperable data assets across cloud platforms.
Domain
Gaming industry, Cloud Data Warehousing, Data Architecture
Required skills
Data modeling (conceptual, logical, physical), Snowflake data modeling, SQL, ERwin/ER/Studio, Lucidchart/Visio, 3NF/Dimensional/Data Vault modeling, Data lifecycle design, Data integration patterns, Data contracts, Architecture trade-off analysis, Data governance
Preferred skills
AI/ML concepts, Storytelling, Mentoring
Technologies
Snowflake, SQL, ERwin, ER/Studio, Lucidchart, Visio, Cloud Data Vault
Responsibilities
Define and evolve enterprise data architecture principles, standards, and build specifications; Create technical development materials including data flow diagrams and system interaction documentation; Design the data lifecycle across the enterprise platform from source systems to consumption; Develop and maintain conceptual, logical, and physical data models in line with enterprise standards; Establish and uphold data modeling standards, naming conventions, and documentation practices; Define and manage conceptual, logical, physical, and semantic data abstraction layers; Apply appropriate 3NF, dimensional, and Data Vault modeling approaches; Align business definitions and data structures with downstream consumption needs; Build and maintain canonical data models that support consistency across systems and domains; Set enterprise data integration patterns for sharing through cloud shares, batch, streaming, APIs, and event-driven methods; Architect interoperability across platforms to enable decoupled, reusable data assets; Specify and manage data contracts and interfaces between data producers and consumers; Evaluate and document architecture trade-offs; Promote data quality, lineage, and governance across data flows; Analyze and profile data to validate assumptions and inform development; Guide and mentor team members on architectural standards and technical build practices.