Senior AI Automation Manager, Business Intelligence

What You’ll Do

• KPI development & ownership: Develop and own the key performance indicators that leadership uses to measure business health, ensuring metrics stay aligned with evolving strategic priorities.
• Strategic & ad-hoc analysis: Partner with stakeholders across Operations, Finance, Product, and Marketing to scope analytical questions, deliver deep-dive insights, and translate findings into clear recommendations that drive business decisions.
• BI development & reporting: Build and maintain dashboards, reports, and data models that make key business metrics accessible, accurate, and trustworthy across the organization.
• Agent design & deployment: Design, prototype, and deploy AI agents that augment BI workflows, including agents that monitor metrics for anomalies, generate narrative summaries, answer ad-hoc data questions, and automate routine reporting cycles.
• Performance monitoring & iteration: Track how deployed agents and dashboards perform in the wild, gather feedback from end users, and continuously refine outputs to improve accuracy, usefulness, and adoption over time.
• Automation opportunity identification: Spot repetitive analytical tasks across the organization and evaluate which are best suited for agentic automation versus traditional BI tooling, balancing impact, complexity, and risk.
• Data quality & integrity: Partner with Data Engineering to validate source data, surface inconsistencies, and ensure that both human-facing analyses and agent outputs are built on a trustworthy foundation.
• Documentation & enablement: Document agent capabilities, limitations, and intended use cases so stakeholders know when to trust outputs and when to escalate or verify.

The Skills and Experience You’ll Bring

• 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 your day-to-day work; comfortable writing clear prompts, evaluating outputs, debugging agent behavior, and knowing when human judgment must override automation.
• Ability to distill analytical work into digestible presentations and communicate learnings clearly.
• Growth mindset and a willingness to work hard – this will be a high-effort, high-reward type role for those excited about building business leverage with AI.

Nice to Haves

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