Principal Architect Data and AI Platforms; Hybrid

Position at Parts Town – Principal Data & Semantic Architect

As the fastest-growing distributor of restaurant equipment, HVAC and residential appliance parts, we like to do things a little differently. First, you need to understand and demonstrate our Core Values with safety being your first priority. That’s key. We’re also looking for unique enthusiasm, high integrity, courage to embrace change…and if you know a few jokes, that puts you on the top of our list!

We have been recognized for our growth and innovation on the Inc. 5000 list 15 years in a row and the Crain’s Fast 50 list ten times. We are honored to have been voted by our Chicagoland team as a Chicago Tribune Top Workplace for the last four years.

Parts Town is building a modern data and AI platform to support advanced analytics and emerging AI use cases across our rapidly growing organization. As a Principal Architect Data and AI Platforms, you will operate at the intersection of data architecture and hands‑on engineering, helping shape how data is modeled, exposed, and used across the business.

This role focuses on owning and improving the core data foundations that power analytics and AI, from warehouse design and dimensional modeling to semantic layer development and system integration. You will work directly in the code and data, defining practical standards, reviewing implementations, and ensuring consistency and quality across the platform.

You will partner closely with data engineers to challenge and refine solutions, ensuring the platform is built for scalability, usability, and reuse. You will also collaborate with product, governance, and security stakeholders to align on definitions, data quality and access patterns so that data can be trusted and consistently used across the organization.

• Design and refine dimensional data models (fact/dimension, star schemas) to support analytics and AI use cases.
• Write, review, and optimise SQL used across the warehouse and semantic layer.
• Build and evolve the semantic or metrics layer to ensure consistent, trusted business definitions.
• Partner directly with data engineers to review implementations, challenge design decisions and improve data pipelines.
• Debug data issues across the stack, from ingestion through transformation to consumption.
• Modify or extend APIs and services to make data and functionality accessible for downstream systems and AI workflows.
• Build connectors or lightweight services that enable systems to interact programmatically.
• Translate business and product requirements into practical data models and system designs.
• Identify gaps in the current architecture and drive pragmatic improvements without over engineering.
• Support AI and agent‑driven use cases by ensuring data is structured, accessible, and reliable.

• 7–10 years of experience in data engineering, analytics engineering or data architecture in production environments.
• Deep, hands‑on SQL skills and experience working directly in complex warehouse environments.
• Experience working across multiple modern data warehouse platforms (Google Big Query, Snowflake, Redshift, Databricks) and understanding their trade‑offs.
• Strong experience modeling data using Kimball‑style dimensional modeling (fact/dimension design, star schemas) and having owned or significantly influenced modeling standards.
• Experience building or maintaining semantic layers or metrics layers that support BI and downstream applications at scale.
• Strong programming skills (Python preferred) and ability to build or modify APIs, services or connectors when needed.
• Experience working closely with data engineers and comfortable reviewing, challenging and improving engineering implementations.
• Understanding of how data moves across systems end‑to‑end, from ingestion through transformation to consumption, and ownership of parts of that lifecycle.
• Comfortable operating in ambiguous environments and taking ownership of the details that make systems actually usable.
• Exposure to AI/ML or LLM‑based workflows and understanding how data platforms need to evolve to support them.

• Hybrid work schedule.
• Team…