Staff Data Architect

FanDuel•New York, NY

6d•$170,000 - $210,000•Hybrid

About The Position

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.

Requirements

7+ years of experience in data architecture, data platforms, and Big Data with demonstrated experience in ML engineering
Deeply technical and analytical, with hands-on architectural experience across Data Warehousing, Streaming, Big Data, and AI/ML systems.
Strong hands-on proficiency with Databricks as a data lakehouse, plus dbt/Spark and modern data modeling.
Demonstrated experience architecting ML and AI workloads, including advanced ML or deep learning, and ideally vector/semantic/unified search, as durable, production-grade systems.
Practical experience using AI tooling and agents to accelerate development while maintaining strong engineering fundamentals: version control discipline, review standards, automated testing, validation gates, and reproducibility.
A systems-first mindset toward AI, treating LLMs and agents as components within well-defined architectural boundaries and governance
Experience leading large-scale data and AI/ML projects in batch and streaming environments.
Ability to influence multiple teams, functional areas, and levels of leadership, and to align stakeholders across organizational boundaries.
Ability to see around corners, predict needs, and identify patterns before they become problems.
Strong data security and compliance awareness.
Excellent leadership, communication, presentation, and facilitation skills.
Bachelor's degree in computer science or a related field.

Responsibilities

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.