Responsibilities
- Define and own the enterprise data architecture strategy, including conceptual, logical, and physical data models across the organization's core domains
- Establish and govern data standards, naming conventions, schema design principles, and modeling best practices used by Data Engineering and Analytics Engineering teams
- Lead the design of scalable, reusable data products in the semantic and analytical layers, ensuring consistency across Decision Science, Data Science, and self-service consumption
- Partner with the Product Data team to align on shared architectural standards, data contracts, and platform decisions—acting as a peer and collaborator, not a dependency
- Evaluate and advise on data platform and tooling decisions (cloud data warehouses, lakehouse patterns, orchestration, metadata management, cataloging)
- Identify and resolve architectural gaps, redundancy, and data quality risks across the data estate
- Contribute to—and in many cases lead—the development of a business glossary, data catalog, and enterprise ontology for key data domains
- Act as a senior advisor to Data Science on data availability, feature engineering infrastructure, and model data requirements
- Collaborate with Decision Science leadership to ensure analytical data models are structured for performance, clarity, and governed self-service
- Champion data governance, lineage, and observability as first-class architectural concerns
- Mentor and guide engineers and analytics engineers on architectural patterns and data modeling best practices
Requirements
- 8+ years of experience in data architecture, data engineering, or a closely related discipline in a complex, multi-team data environment
- Demonstrated experience designing and governing enterprise data models across transactional, analytical, and semantic layers
- Deep expertise in modern data stack patterns: cloud data warehouses (Snowflake, BigQuery, Databricks), lakehouse architectures, dbt, data cataloging tools
- Strong command of data modeling methodologies—dimensional modeling, Data Vault, OBT, and when to apply each
- Experience establishing or evolving data governance programs including metadata management, lineage, and data quality frameworks
- Ability to work across technical and business stakeholders—translating architectural decisions into clear business value
- Experience partnering with Data Science teams on feature engineering, training datasets, or MLOps data infrastructure
- Excellent communication and documentation skills; you write clearly about architecture for both technical and executive audiences
- Experience working in matrix or cross-functional environments, navigating organizational boundaries without direct authority
Nice to Have
- Experience in a company with both a centralized data function and an embedded Product Data or Data Platform team
- Familiarity with semantic layer tools (Cube, MetricFlow, LookML) and headless BI patterns
- Background in data mesh, data product, or federated data governance operating models
- Exposure to real-time or streaming data architecture (Kafka, Flink, Spark Streaming)
- Experience with data privacy-by-design architecture and regulatory frameworks (GDPR, CCPA)
Benefits
- Competitive compensation, plus all full-time employees participate in our ownership program - because everyone should have a stake in our success.
- Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.
- Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.
- Comprehensive wellness programs and mental health support
- Learning and development resources, including professional development tools and tuition reimbursement, to support your growth
- The technology and tools you need to do your best work
- Motivosity employee recognition program
- A culture rooted in inclusivity, support, and meaningful connection
Team
Structure: Data Engineering, Analytics Engineering, Data Science, Decision Science
Additional Information
- All employees must pass a background check as part of the hiring process.
- To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws. Any attempt to misrepresent personal or professional information will result in disqualification.
