Senior AI Automation Manager, Business Intelligence

DAT•Seattle, WA

6d•$141,000 - $190,000•Hybrid

About The Position

DAT Freight & Analytics is seeking a Senior AI Automation Manager, Business Intelligence to join their Carrier team. This hybrid role focuses on the intersection of analytics and applied AI, aiming to redefine business intelligence at DAT. The successful candidate will deliver critical insights for strategic decisions, design and deploy AI agents to scale BI capabilities, and partner with stakeholders to identify high-value problems. Responsibilities include developing and reporting on KPIs, building agentic workflows to automate analytical tasks, and shaping the integration of AI agents into the BI function. This is a high-impact individual contributor role with significant leadership visibility.

Requirements

3–8 years in a business intelligence, data analytics, or analytical consulting role with a track record of turning data into decisions.
Demonstrated ability to translate ambiguous business questions into structured analyses and clear recommendations.
Fluency with AI tools in day-to-day work, including writing clear prompts, evaluating outputs, debugging agent behavior, and knowing when human judgment is necessary.
Ability to distill analytical work into digestible presentations and communicate learnings clearly.
Growth mindset and a willingness to work hard.

Nice To Haves

Experience deploying AI agents or LLM-powered tools, including evaluation, monitoring, and failure modes.
Experience in fintech, payments, banking, or logistics.

Responsibilities

Develop and own key performance indicators (KPIs) that leadership uses to measure business health, ensuring metrics align with evolving strategic priorities.
Partner with stakeholders across Operations, Finance, Product, and Marketing to scope analytical questions, deliver deep-dive insights, and translate findings into clear recommendations.
Build and maintain dashboards, reports, and data models to make key business metrics accessible, accurate, and trustworthy.
Design, prototype, and deploy AI agents that augment BI workflows, such as agents that monitor metrics for anomalies, generate narrative summaries, answer ad-hoc data questions, and automate routine reporting.
Track the performance of deployed agents and dashboards, gather user feedback, and continuously refine outputs.
Identify repetitive analytical tasks across the organization and evaluate their suitability for agentic automation.
Partner with Data Engineering to validate source data, surface inconsistencies, and ensure data integrity for both human-facing analyses and agent outputs.
Document agent capabilities, limitations, and intended use cases for stakeholders.