Principal AI Engineer, Data

What You'll Do (Essential Functions)

• Own data problems end-to-end — take a problem from hypothesis through validated, near‑production prototype. Define what done looks like and get there.
• Build customer‑ready prototypes and applications — working software for vertical data offerings, new data products, and internal tooling. Not specs. Not briefs. Working software.
• Build and maintain shared infrastructure, patterns, and tooling that let Outcome Owners across all pods move faster. Every problem you solve should leave behind something reusable.
• Work closely with Dev Ops and Engineering teams to ensure prototypes respect security standards and deployment paradigms — enabling smooth handoff when ready to scale.
• Bridge Data Products and Engineering — translate between data product judgment and engineering architecture without losing fidelity in either direction.
• Drive adoption of tools and capabilities you build through evangelism, documentation, and enablement. Shipping something nobody uses isn't an outcome.
• Identify and go after high‑leverage opportunities across the portfolio proactively — infrastructure bottlenecks, tooling gaps, automation opportunities — without being asked.
• Use AI tools daily as your execution layer — Claude Code and similar — with the judgment to know when AI output is production‑ready vs. when it needs human intervention.

• 8+ years in data engineering, ML engineering, backend systems, platform engineering, or a technical product role with hands‑on building experience
• Can build production‑quality prototypes and applications — not just define them. Writes code, builds pipelines, architects systems.
• Has built customer‑facing data products or applications, not just internal pipelines
• Experience working with Dev Ops and Engineering teams to hand off systems cleanly — understands security standards, deployment paradigms, and what "ready to scale" means from the receiving team's perspective
• Systems thinking from day one — prototypes account for scale, failure modes, monitoring, and data drift
• Platform mindset — instinctively builds reusable infrastructure, not one‑off solutions
• Can own a problem end‑to‑end: hypothesis, prototype, validation, QA, handoff — not just the technical implementation
• Fluency with AI coding tools and the judgment to know when AI output is production‑ready vs. when it needs human intervention
• Data judgment — understands what good data looks like, not just how to move it around
• Strong communicator who can translate between data product teams and engineering teams without losing fidelity in either direction
• Proactive orientation — identifies leverage opportunities across a portfolio, doesn't wait for inbound requests
• B2B SaaS experience, preferably in data platforms, developer tools, or data operations

• Background spanning data engineering and product management — has operated at the intersection
• Familiarity with modern data stack tooling (Google Cloud, Snowflake, Databricks, etc)
• Has done the full cycle at least once — raw hypothesis to production‑ready, customer‑facing output without a handoff chain
• Experience building AI‑powered automation or self‑remediation capabilities
• Knowledge of data privacy regulations (GDPR, CCPA) and their product implications

The senior engineer who is done building to someone else’s spec. Who has watched the gap between what gets designed and what gets built — and wants to close it by being the person who does both. You have the technical depth to build real, customer‑ready systems and the product instinct to know which systems matter. You care about security and operational standards not because someone told you to, but because you’ve been on the receiving end of a bad handoff and refuse to be the person who causes one.

You want to work at the frontier — new data products, vertical offerings, things that don’t exist yet — with AI as your force multiplier. You don’t want to maintain; you want to create. And you want what you create to actually make it to production, not die in a prototype graveyard.

US base salary: $171,500—$269,500 USD