Senior Data Architect

About Goods & Services
  
Goods & Services is a product design and engineering company. 

We solve mission-critical challenges for some of the world’s largest enterprises, with deep expertise in highly regulated industries—including life sciences and financial services. Our design-led approach allows us to apply cutting-edge capabilities in AI, Data and Hardware Engineering to companies of any size. 

Headquartered in the United States, we operate regional development centers in Mexico and the United Kingdom. This global footprint—anchored by our nearshore model—enables us to deliver at scale with the speed, efficiency, and cultural alignment our clients expect.

About the job
  
Goods & Services is looking for a Senior Data Architect responsible for defining, governing, and evolving the enterprise data architecture that powers our Data Engineering, Analytics Engineering, Data Science, and Decision Science teams—while maintaining a close strategic partnership with our Product Data organization. You will serve as the connective tissue between how data is produced and how it is consumed—ensuring that our data assets are reliable, well-modeled, semantically consistent, and designed to scale.

What you’ll do:

• 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

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 

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)