Senior Data Architect

This is a fully remote position, open to applicants in United States.

📋 Description

• Define and take ownership of the enterprise data architecture strategy, encompassing conceptual, logical, and physical data models across the organization's main domains.

• Establish and oversee data standards, naming conventions, schema design principles, and modeling best practices utilized by the Data Engineering and Analytics Engineering teams.

• Lead the creation of scalable, reusable data products within the semantic and analytical layers, ensuring consistency across Decision Science, Data Science, and self-service consumption.

• Collaborate with the Product Data team to ensure alignment on shared architectural standards, data contracts, and platform decisions—acting as a peer and collaborator rather than a dependency.

• Assess and provide guidance on data platform and tooling choices (cloud data warehouses, lakehouse patterns, orchestration, metadata management, cataloging).

• Identify and address architectural gaps, redundancies, and data quality risks throughout the data estate.

• Contribute to—and often lead—the development of a business glossary, data catalog, and enterprise ontology for essential data domains.

• Serve as a senior advisor to Data Science regarding data availability, feature engineering infrastructure, and model data requirements.

• Work alongside Decision Science leadership to ensure analytical data models are designed for performance, clarity, and governed self-service.

• Advocate for data governance, lineage, and observability as primary architectural concerns.

⛳️ Requirements

• Over 5 years of experience in data architecture, data engineering, or a closely related field within a complex, multi-team data environment.

• Proven experience in designing and governing enterprise data models across transactional, analytical, and semantic layers.

• Extensive knowledge of modern data stack patterns: cloud data warehouses (Snowflake, BigQuery, Databricks), lakehouse architectures, dbt, and data cataloging tools.

• Strong understanding of data modeling methodologies—dimensional modeling, Data Vault, OBT, and the appropriate context for their application.

• Experience in establishing or enhancing data governance programs, including metadata management, lineage, and data quality frameworks.

• Ability to collaborate across technical and business stakeholders—translating architectural decisions into tangible business value.

• Experience in partnering with Data Science teams on feature engineering, training datasets, or MLOps data infrastructure.

• Exceptional communication and documentation skills; capable of articulating architectural concepts clearly to both technical and executive audiences.

• Experience working in matrix or cross-functional settings, adept at navigating organizational boundaries without direct authority.

🏝️ Benefits

• Opportunities for professional development.