Director, Data Engineering

Responsibilities

• Firefighter Mode: Leadership asks "what's the ROI of [new AI tool]?" on Wednesday. You have an answer by Friday.
• Validate (or invalidate) a team’s claim that their AI initiative saved 10,000 hours.
• Rapidly instrument, measure, and communicate whether an initiative is working.
• Jump into whatever is urgent and ambiguous—and you close it.
• Systems Thinker Mode: Build measurement frameworks that work across wildly different AI tools and use cases.
• Create dashboards, workspaces, semantic models, and self-serve layers that let stakeholders understand progress.
• Scale the data pipelines and instrumentation that capture agent telemetry, usage signals, and outcome metrics.
• Shape the strategy for how we think about productivity, time savings, and quality improvements in an AI-augmented workforce.
• Influence how Analytics (and business functions) evolve their operating models, job profiles, and organization structures for the AI era.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• AI power user. You’re already a power AI user in your day-to-day work.
• 0→1 builder experience. You’ve built measurement systems from scratch in ambiguous spaces.
• Speed + rigor. You can move fast without being sloppy. You know when to be 60% right now vs. 95% right later.
• Executive communication. You’ll regularly present to leadership. Your insights need to be crisp, actionable, and defensible.
• Cross-functional influence. You’ll work across dozens of teams (EE, Security, CPP, DevInfra, and more). You need to drive alignment without authority.
• Honesty. Some AI initiatives won’t work. Some claimed impact will be inflated. You’ll need to call it like you perceive it.