Responsibilities
- Define and govern shared operational data models and classification frameworks used across multiple teams and systems; establish architectural standards designed to scale beyond any single team or program
- Translate business requirements into data architecture designs, mapping how information should be structured, stored, and integrated to drive informed decision-making and measurable outcomes
- Develop a deep understanding of how data flows between operational systems to identify gaps, inconsistencies, and opportunities for standardization
- Drive cross-functional alignment on shared definitions, integration standards, and field-level agreements between teams where no single team has unilateral authority
- Manage schema evolution and breaking changes across dependent systems, including deprecation planning, coordinating dependent teams through transitions, and stakeholder communication
- Partner with data science and engineering teams to translate architectural decisions into production-grade data products and pipelines
- Design cost attribution and resource modeling that ensures budget and workforce data reconciles end-to-end
- Anticipate future data needs across operational teams and proactively design canonical structures before teams build in isolation
- Set the long-term data architecture strategy for GO, shaping roadmaps across product, engineering, and operations partners
- Enable the development of internal tools and decision systems by defining the data requirements, specifications, and governance frameworks they depend on
- Develop data architecture capability across the broader organization by mentoring program managers and embedding architectural thinking in teams across GO
Minimum Qualifications
- Bachelor's degree in a directly related field, or equivalent practical experience
- BA/BSc degree in a quantitative, technical, or related field, or equivalent practical experience
- 7+ years of program management or technical program management experience in a data-intensive technical environment
- Demonstrated experience designing data models, taxonomies, or classification systems adopted across multiple teams or organizations
- Ability to reason about data structures, schemas, pipeline architecture, and system integrations, including translating these concepts across technical and non-technical audiences
- Proven track record of driving cross-functional consensus on shared standards or definitions, including with director- or VP-level stakeholders
- Experience translating complex technical decisions for executive audiences and driving adoption across organizational boundaries
- Experience leading schema governance or data standards programs end-to-end, including deprecation planning and migration coordination
- Analytical thinking and structured problem-solving at scale
- Experience working across global, multicultural teams
Preferred Qualifications
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience building internal tools or decision systems from requirements through adoption (not just consuming them)
- Experience working in a technology company or fast-paced global operations environment
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience with data pipeline orchestration, warehouse architecture, or ETL design
- Experience with operational data in customer support, content moderation, trust & safety, or workforce management domains
- Background in cost attribution, budget modeling, or operational finance
- Experience managing schema governance and breaking changes across a large, distributed engineering organization
- Proficiency in SQL and relational databases
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Track record of establishing data governance frameworks or canonical standards adopted at org or company scale
- Prior experience building or scaling a data architecture function from early stage
$123,000/year to $179,000/year + bonus + equity + benefits
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