Making things better by making better things
Senior Data Architect
Location
United States
Posted
5 days ago
Salary
0
Seniority
Senior
Job Description
Senior Data Architect
Goods & Services
• 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
Job Requirements
- 5+ 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
Benefits
- Professional development opportunities
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