Staff Data Systems Architect
Core
Own the end-to-end architecture of a modern product data stack supporting a large-scale digital platform, shaping the roadmap and establishing standards for data movement, governance, and access.
Role type
Staff Data Systems Architect
Builds
Scalable, secure, cost-conscious data infrastructure and AI-ready data practices for business and product teams
Domain
Technology / Data Engineering / Cloud Platforms
Deliverable
production ML models | infrastructure
Required skills
End-to-end data platform architecture, Cloud data platforms (Snowflake), Vendor evaluation and consolidation, Data governance and compliance, Infrastructure cost management, Technical standards definition, Cross-functional influence
Preferred skills
Semantic layers (dbt), Data cataloging (Secoda/Snowflake Horizon), Experimentation pipelines, Consumer subscription metrics, AI assistant data environments
Technologies
Snowflake, dbt, Secoda, Snowflake Horizon
Responsibilities
Own the end-to-end systems architecture for the product data stack and maintain a clear roadmap for its evolution; Define and enforce standards for data movement, integration, organization, and standardization across the data ecosystem; Lead architecture reviews for new technology and tooling decisions, evaluating vendors against the existing stack; Collaborate with technical teams and vendors on proof-of-value evaluations, platform migrations, consolidations, and technology adoption decisions; Contribute to data compliance architecture, including PII tagging, data governance, and deletion/data subject request (DSR) policies; Administer access and licensing across the data stack while maintaining visibility into platform usage, permissions, and vendor spend; Establish and communicate architecture decisions, technical standards, ownership models, and governance practices across the organization; Drive automation that enforces architecture and data standards, reducing reliance on manual reviews; Balance infrastructure performance, reliability, scalability, security, and cost when making architecture and platform decisions; Help establish governed approaches for making data safely available to AI assistants and agents, including AI-ready data models, context, and access guardrails
Seniority
Staff, strategic architecture & cross-org influence
