About the Role:

This is a high-impact opportunity for a Data Architect who wants to do more than design models in isolation. You will help shape how a global enterprise organizes, governs, and publishes data as trusted products. You will work on a modern cloud platform built around Snowflake, Databricks, Azure, medallion architecture, and domain-oriented data products—while partnering with teams across engineering, governance, analytics, BI, and the business.

The right candidate will enjoy solving complex, real-world data problems at enterprise scale. You will connect source systems, ingestion patterns, data quality, business rules, dimensional modeling, semantic models, governance, and consumption into practical architecture that people can trust and reuse. This role is ideal for someone who is technically strong, collaborative, pragmatic, and excited to help build standards, patterns, and data products that will be used across a global business.

Why This Role Matters:

We are moving from legacy, project-based data delivery to a modern, product-oriented data platform. That means this role has real influence: you will help define the patterns that determine how data moves from source systems into governed enterprise models and ultimately into reports, analytics, AI/ML, and business decisions. Your work will not sit on a shelf—it will shape how teams across the company understand performance, serve customers, optimize operations, and make better decisions every day.

Our platform uses Snowflake as the governed enterprise data platform for trusted data products, Databricks for AI/ML and advanced analytics patterns where appropriate, Azure for cloud infrastructure, and a four-layer medallion model. Raw preserves source fidelity for auditability and replay. Cleansed applies technical standardization while retaining traceability. Refined defines the trusted enterprise representation of facts, dimensions, keys, relationships, and business rules. Curated publishes reusable, business-facing data products for reporting, self-service analytics, semantic models, and future AI-enabled consumption.

Key Responsibilities:

• Design practical, scalable data models across enterprise data domains, with emphasis on dimensional models, conformed dimensions, certified facts, business keys, surrogate keys, historical tracking, and semantic-ready structures.
• Lead Refined layer model design so core business concepts are modeled consistently and can be reused across domains, dashboards, analytics, AI/ML, and future semantic layer capabilities.
• Translate source system behavior into clear source-to-target mappings across Raw, Cleansed, Refined, and Curated layers, including data availability, ingestion patterns, business rules, transformation logic, and quality expectations.
• Partner with Data Governance to align models with schema standards, naming standards, ownership, stewardship, classification, privacy, retention, lineage, metadata, glossary, certification, and access requirements.
• Guide teams on where business rules and quality controls belong across the platform so logic is not duplicated inconsistently across reports, notebooks, SQL views, semantic models, or downstream tools.
• Work with Data Engineering and Platform Engineering to turn approved designs into reliable, scalable implementations in Snowflake, Databricks, Azure, ERWIN, CI/CD pipelines, and governed deployment processes.
• Partner with Technical Data Product Owners, BI, Analytics Engineering, business SMEs, and source system owners to turn business needs into durable, reusable data structures rather than one-off reporting logic.
• Participate in architecture and model reviews, document key design decisions, and contribute to reusable playbooks, standards, templates, and migration patterns for future data products.
• Coach and influence engineers, analysts, and cross-functional partners on medallion architecture, dimensional modeling, data product thinking, and practical source-to-consumption design.

What You Bring

• Bachelor’s or master’s degree in Computer Science, Information Technology, Data Engineering, Data Architecture, Engineering, or a related field.
• 5+ years of experience in data architecture, data modeling, data warehousing, data engineering, or enterprise data platform design.
• Strong hands-on experience designing dimensional models, including facts, dimensions, conformed dimensions, grain definition, slowly changing dimensions, surrogate keys, business keys, and semantic-ready structures.
• Practical experience with medallion architecture, lakehouse architecture, enterprise data warehouses, data lakes, and modern cloud data platforms.
• Ability to analyze source systems, define source-to-target mappings, profile data, document business rules, and clarify ingestion, quality, and transformation requirements.
• Strong SQL skills and the ability to review, interpret, and guide transformation logic across engineering and analytics teams.
• Experience with Snowflake, Databricks, Azure Data Factory, Azure Data Lake Storage, or comparable modern cloud data platform technologies.
• Experience with data modeling and metadata tools such as ERWIN, ER/Studio, or comparable platforms.
• Understanding of batch, streaming, CDC, API-based, file-based, incremental, merge, append-only, and historical load patterns.
• A collaborative style, strong judgment, and the ability to influence decisions across business, technical, governance, architecture, and engineering teams.
• Clear communication skills with the ability to explain complex data architecture concepts in practical, business-relevant language.

Nice to Have

• Experience in retail, convenience, fuel, merchandising, loyalty, supply chain, finance, store operations, or customer/digital data domains.
• Hands-on experience with Snowflake architecture, schema design, performance patterns, governance capabilities, Horizon Catalog, or Iceberg/open table formats.
• Hands-on experience with Databricks, Spark, PySpark, Delta Lake, Unity Catalog, or advanced analytics environments.
• Experience supporting semantic layer design, Power BI semantic models, metrics layers, governed BI consumption, or reusable data product publication.
• Experience with CI/CD, Git, Azure DevOps, Terraform, infrastructure-as-code, and automated deployment practices for data platforms.
• Experience working in Agile delivery models, product-based operating models, domain-aligned squads, or scaled data platform programs.
• Certifications in Snowflake, Databricks, Azure, data architecture, or related technologies.

Apply now