Principal AI Engineer, Data

ZoomInfo Technologies LLC•Waltham, MA

2d•$171,500 - $269,500•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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.