Job Description

ZoomInfo is seeking a Principal AI Engineer to own data problems end-to-end — from hypothesis through customer-ready, near-production prototype — and build the infrastructure that lets the entire Data Products org deliver outcomes faster. This person combines deep technical execution with product instinct: they don't just build things that work, they build things that solve real customer problems and drive measurable business impact.

This is a senior technical builder with a product mindset. You will use AI tools and direct engineering skill to produce working software — prototypes and applications that are customer-ready, not demos. But you also bring the judgment to know which problems are worth solving, how customers experience our data, and where the highest-leverage opportunities sit across the portfolio. You talk to customers, you dig into usage patterns, you understand the pain - and then you go build the solution yourself.

You work closely with DevOps and Engineering to ensure security standards are met, established paradigms are respected, and everything you build can be handed off smoothly when ready to scale. You enable fast delivery of vertical data offerings, new data products, and internal tooling by being the person who figures it out by building.

You don't just solve one problem at a time. You build the patterns, tooling, and architecture that make every subsequent problem cheaper to solve. And you measure success not by what you shipped, but by the customer outcomes it drove.

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 DevOps 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.

What You Bring

Required Qualifications

• 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 DevOps 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

Preferred Qualifications

• 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

Who Thrives Here

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.

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