Sr. Data Architect

YETI•Austin, TX

About The Position

At YETI, we believe that time spent outdoors matters more than ever and our gear can make that time extraordinary. When you work here, you’ll have the opportunity to create exceptional, meaningful work and problem solve with innovative team members by your side. Together, you’ll help our customers get the high-quality gear they need to make the most of their adventures. We are BUILT FOR THE WILD™. The Senior Data Architect is a strategic and hands-on leader responsible for designing and delivering YETI’s next-generation data and AI architecture across retail, eCommerce, supply chain, and operations. This role will define scalable modern data platforms, domain-driven data models, and agentic AI capabilities leveraging cloud-native technologies (Azure and/or GCP), Databricks (including Agent Bricks), Google BigQuery, Google Vertex AI, SAP S/4HANA, SAP Datasphere, and Power BI.

Requirements

10+ years of experience in data architecture, data engineering, and analytics.
Strong hands-on architecture experience with cloud platforms (Azure and/or GCP).
Deep experience designing and implementing Databricks Lakehouse architectures.
Experience with Google BigQuery enterprise warehouse technologies.
SAP S/4 integration experience with Databricks and BigQuery.
Proven ability to create retail-specific enterprise domain data models and canonical data definitions.
Expertise in ingestion frameworks (batch/CDC/streaming), data pipeline observability, and performance optimization.
Strong experience with data governance and data quality frameworks (policies, controls, stewardship, monitoring).
Experience with enterprise BI tools, including Power BI.
Demonstrated experience contributing to data strategy and/or AI strategy, including delivery roadmaps and measurable outcomes.

Nice To Haves

Experience with Databricks Agent Bricks AI capabilities.
Experience with retail/eCommerce analytics (Customer 360, omnichannel metrics, merchandising, fulfillment, inventory optimization).
Experience with data product or data mesh operating models.
Experience with event-driven architectures and modern integration patterns for SaaS ecosystems.
Experience building or architecting AI agents (LLM-based workflows), including RAG and tool integration patterns.

Responsibilities

Define and lead enterprise data architecture aligned to YETI business priorities.
Design modern Lakehouse and analytical architectures leveraging Databricks and/or Google BigQuery.
Establish reusable architecture patterns (Medallion layers: Bronze/Silver/Gold, semantic layers, and data products).
Create reference architectures and standards for batch, streaming, and near-real-time analytics and AI workloads.
Design and govern enterprise domain data models for key retail domains: Customer 360, Product & Merchandising, Orders & Fulfillment, Inventory, Supply Chain, and Finance.
Apply domain-driven design (DDD) and data product concepts to improve reuse, ownership, and scalability.
Align SAP and non-SAP canonical models and definitions to drive consistent KPIs and interoperable analytics.
Architect secure, scalable data platform solutions on Azure and/or GCP (storage, compute, networking, and identity).
Define cross-platform patterns for coexistence and integration between Databricks and BigQuery where applicable.
Partner with security and infrastructure teams to implement network isolation, secrets management, and compliance controls.
Design ingestion frameworks for batch, CDC, and streaming/event-driven integration.
Establish integration patterns for SAP S/4HANA and SAP BDC Datasphere into data platform.
Define standards for API ingestion, file ingestion, incremental loads, schema evolution, and observability.
Partner with leadership to define data strategy and AI strategy, including the roadmap for scalable GenAI and agentic capabilities.
Architect and support the development of AI agents using Databricks Agent Bricks, including RAG patterns and tool orchestration.
Define governance patterns for AI agents (identity, permissions, auditing, cost attribution, and guardrails) aligned with enterprise policies.
Implement enterprise data governance frameworks (e.g., Unity Catalog and complementary tooling) for access control, lineage, and auditability.
Define and operationalize data quality frameworks: validation rules, monitoring, alerting, and SLA-driven pipelines.
Establish standards for metadata management, data contracts, and stewardship across domains.
Lead the architectural enablement of self-service analytics using Power BI and curated semantic models.
Standardize KPI definitions and calculation logic to ensure consistent reporting across regions and functions.
Partner with business stakeholders to deliver executive dashboards and operational reporting with trusted metrics.
Influence and align cross-functional teams (IT, Data Engineering, Analytics, Security, and Business) on architecture standards and priorities.
Mentor engineers and analysts; raise platform maturity through best practices, design reviews, and documentation.
Drive adoption of platform capabilities through enablement, patterns, and reusable accelerators.