Forward Deployed Data Engineer

ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast. The Role ZoomInfo is building its Forward Deployed Engineering function. You will define how it operates — how engagements run, what the deliverables are, how the playbook works, what scales and what doesn't. The support structure exists (data team, product team, infrastructure, executive sponsorship); what's missing is the person who brings it to life in front of customers and turns early wins into a repeatable model. As an FDE, you embed directly with ZoomInfo's strategic accounts — large enterprises with complex data needs, often in financial services, insurance, technology, and other globally distributed industries. You work alongside their teams to understand their go-to-market challenges, then design and deliver bespoke intelligence applications that combine ZoomInfo's third-party data with the customer's first-party data to drive real business outcomes. You own the engagement end-to-end: from discovery through deployment, from executive presentation through production code. You'll work closely with our data and product teams to bring the full breadth of ZoomInfo's data foundation to bear — company intelligence, contact data, buying signals, intent data, and specialized vertical datasets — assembled into purpose-built applications tailored to each customer's specific personas and workflows. ZoomInfo has one of the deepest go-to-market data foundations in the world — 500M+ professional profiles, 100M+ company records, intent signals, vertical datasets spanning financial filings, insurance, commercial fleet, and more. You'll have access to incredible data, powerful infrastructure, and our most important customer relationships. What you build with them — and how you build it — will define the model going forward. What Engagements Typically Look Like A few recurring patterns show up across strategic accounts. Most engagements involve some combination of: Entity resolution at scale — reconciling legal entity hierarchies (D&B, tax IDs, company-house registrations) with how customers actually go to market. Multinationals with hundreds of legal entities collapsing to a single GTM record. Hierarchy management — enforcing one-to-one matching across regions, fixing parent-child linkage gaps, dispositioning orphaned accounts, surfacing white space on top of clean parent IDs. Location-level precision — moving customers off monolithic HQ-level enrichment so geo-based sales teams see local firmographics instead of global rollups. Automated, no-human-in-the-loop logic — entity suppression, disposition-based matching, orchestration rules for inactive entities, parent linkages, and white space alerts. Data warehouse as the operating layer — moving sophisticated analysis out of CRM (Salesforce can't do hierarchy work at scale) into Snowflake or BigQuery, via API or data cube depending on the workflow. Buying group filtering — applying persona-density criteria across hierarchies to turn a customer's 5,600 Disney legal entities into 31 actionable targets. Two recent engagements as concrete reference points: A global infrastructure customer brought us 1.8M records and 300K flagged as "unmatched," with a data team of one. An automated domain validation pipeline reframed the problem entirely — 175K inactive sites, 30K redirects, 65K real opportunities — and turned a "coverage gap" conversation into a data quality program. An enterprise planning platform had 120K Salesforce accounts, broken hierarchies, and field leaders reporting near-zero confidence in their enrichment data. The underlying data was accurate; the matching was wrong. Custom disposition logic on a 10K-account priority sample produced 7,444 high-confidence matches at 98–99% accuracy — validated before scaling the framework to the full universe. This is the work. It moves between data engineering, applied product development, and stakeholder management — often in the same week. What You'll Build Every engagement comprises a consistent service architecture — three pillars built on top of each other, and five capability areas assembled into the actual deliverable. Three pillars: Data Foundation — golden reference matching, persistent IDs, unified entity profiles across the customer's first-party systems. Data Management — business-specific logic that turns the foundation into something the customer can actually go to market with: customer definitions, account models, entity resolution. Activation — TAM to SAM to SOM, fit scoring, in-market signals. Five capability areas: Data Foundation Development — Match every record across the customer's CRM, ERP, billing, and marketing systems to a golden reference dataset. Build custom disposition logic, domain validation, marketability classification, and legal entity crosswalks. The output is a single persistent ID linking every system. Account Architecture & Entity Resolution — Define what an account means for the customer's business (address-based, country, HQ, ultimate-parent rollup, or hybrid), then build automated logic that enforces that structure: duplicate resolution, inactive entity disposition, hierarchy linkages. TAM Development & White Space Discovery — Build the customer's complete addressable market against ICP criteria, suppress against existing customers via ultimate-parent rollup, surface white space inside customer hierarchies, and apply buying group filters to reduce overwhelming entity volume to targeted pipeline. Account Fit Scoring & In-Market Signals — Build custom fit models from historical win/loss patterns; configure both evergreen signals (executive moves, leadership changes, fundin