Data Architect

**THE POSITION
**Our roster has an opening with your name on it

As a Staff Data Architect within Data Architecture who focuses on AI & ML, you will design how the company gets maximum leverage out of  its data through ML and AI. This role spans multiple domains and sits at the intersection of our data platform and the AI/ML capabilities built on top of it. You will own the architectural direction for both using AI to make our pipelines/platform more productive and for building AI native capabiiilities that turn our data assets into differentiated products.

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
Everyone on our team has a part to play

• Own the architectural vision for applying AI across the data platform — both to accelerate how data is ingested, modeled, and delivered, and to unlock new capabilities from our data assets.
• Architect an agentic, AI-assisted "data pipeline factory" — the patterns, guardrails, and standards that let AI and agents build, test, and maintain production data pipelines with strong engineering discipline.
• Design and evolve AI-native tools and services that take advantage of the company's data assets, ensuring they are built on sound, reusable architectural foundations rather than one-off solutions.
• Define the architecture and scope for advanced ML and deep learning workloads on the data platform, including vector and unified search across the company's data.
• Establish reference architectures, contracts, and standards that keep ML and AI data flows interoperable, observable, reproducible, and ML-ready by default.
• Prevent fragmentation and duplication across AI and ML efforts by representing the data organization's end-to-end view and driving convergence on shared patterns.
• Partner with data, ML, and engineering teams to translate ambitious AI and data ideas into concrete, buildable architecture, bridging strategy and execution.
• Evaluate emerging AI, ML, and data technologies and recommend where the company should adopt, standardize, or invest to create durable advantage.
• Oversee the performance, cost, and reliability of AI and ML data systems, continually improving efficiency as they scale.
• Communicate the value and direction of this work to stakeholders across and beyond the data organization, building the alignment needed to move it forward.

THE STATS
What we're looking for in our next teammate