Data Architect

Role Overview

• 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

Tech Stack

• BigQuery
• Cloud
• Vault

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
• Annual learning and development stipends 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