Senior Data Architect

Goods & Services

Senior Data Architect defining and evolving the data architecture at Goods & Services. Collaborating across teams for effective data governance and reliability in data usage.

Posted 6/29/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

BigQueryCloudVault

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Comp & perks

Professional development opportunities

ATS Keywords

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Hard Skills & Tools

Data ArchitectureData EngineeringData ModelingFeature EngineeringMLOps Data Infrastructure

Soft Skills

Excellent CommunicationDocumentation SkillsStakeholder Management