Staff Data Systems Architect

Role overview

Lead the end-to-end architecture of a modern product data ecosystem supporting analytics, experimentation, attribution, marketing activation, and data science. This staff-level role combines systems design, technical leadership, governance, vendor evaluation, and cross-functional coordination to make the data platform scalable, secure, cost-conscious, and easier for teams to use.

Responsibilities

- Own the architecture and roadmap for the product data stack, including how data moves between platforms and teams. - Coordinate platform contributions across data engineering, business intelligence, product analytics, backend engineering, and related stakeholders. - Establish standards for data movement, normalization, ownership, documentation, access, and lifecycle management. - Lead evaluations of new tools and vendors, identify duplication, and guide proof-of-value efforts, consolidations, and migrations. - Define compliance patterns for personally identifiable information, including tagging, deletion, and data-subject request workflows across destinations. - Maintain visibility into platform access, licensing, infrastructure usage, and vendor spend while balancing reliability, performance, and cost.

Requirements

- Approximately 8–12 or more years in systems design, architecture, or engineering, including 3–5 years designing end-to-end data platforms at staff or architect level. - Experience integrating a modern customer data stack covering product analytics, experimentation, attribution, and marketing activation. - Strong experience with a cloud data platform such as Snowflake, including performance tuning, cost optimization, and role-based access design. - Background implementing data governance, security, and compliance frameworks across multiple systems, with practical PII and deletion controls. - Demonstrated ability to define architecture standards adopted across an organization and enforce them through automation. - Proven ability to drive technical decisions across teams, communicate clearly, and influence without relying solely on formal authority.

Nice to have

- Experience with semantic and metrics layers using dbt or comparable tooling, data catalogs, metadata stewardship, experimentation governance, and advanced SQL. - Familiarity with consumer subscription products and acquisition, retention, and engagement metrics. - Experience making governed data safely available to AI assistants or agents through AI-ready models, contextual information, and guardrailed access patterns.

Benefits and work setup

- The source describes mentorship, learning and development resources, wellness support, family-related leave and fertility assistance, healthcare coverage, retirement savings with employer matching, recognition programs, and access to a health and fitness service.