Forward Deployed Data Engineer

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., 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, funding rounds) and tailored ones (intent topic spikes, senior job postings, technology displacement events).
• Ongoing Governance & Automation - Match orchestration rules, enrichment segmentation, CRM field locking, and warehouse integration (Snowflake, BigQuery) that keep the foundation clean as the business evolves.

Most engagements use at least three of these. Your job is to diagnose which the customer actually needs, scope the work, and deliver.

What You’ll Do

Own Strategic Customer Engagements End-to-End Serve as the primary technical point of contact for assigned strategic accounts. Run discovery sessions with data engineers, sales operations leaders, and revenue executives. Diagnose the underlying problem (which is rarely the one the customer first describes - “we have a coverage gap” often turns out to be a marketability problem, “we don’t trust the data” often turns out to be a matching problem). Scope the use case, design the solution, build the application, deploy it in the customer’s environment, and stay accountable for the outcome - not just the delivery.

Bridge Technical and Business Audiences Sit with the sales team. Present to executive leadership. Synthesize complex go-to-market data needs into clear, actionable proposals - then deliver the solution. You’re equally comfortable whiteboarding matching architecture with a data engineering team, walking a sales operations director through disposition codes, and presenting ROI to a CRO.

Build the FDE Playbook Document what works: discovery frameworks, engagement phases, integration patterns, deliverable templates, success metrics. The current strategic-account engagements are reference patterns - your job is to extract what’s repeatable from each new account and turn it into a model that scales to additional strategic accounts, verticals, and eventually a team. Feed field learnings back to product, engineering, and data teams to inform product direction and dataset priorities.

Drive Stickiness and Expansion Every application you build embeds ZoomInfo more deeply into the customer’s daily workflows. Identify expansion opportunities as they emerge - new use cases, new personas, new datasets, displacement opportunities against incumbent providers.

Do you have experience in Stakeholder relationship building?, High Ownership, High Ambiguity Tolerance This role doesn’t exist yet at ZoomInfo. You take ownership of outcomes - not tasks - and you’re comfortable making judgment calls with incomplete information, building process where there is none, and figuring things out as you go.

Strong Software and Data Engineering Fundamentals You write production-quality code. You’re proficient in Python and SQL, and comfortable working in cloud data warehouses (Snowflake, BigQuery, Databricks, or similar). You’ve built or substantially worked with: data pipelines, entity matching or deduplication logic, API integrations, and applications that real users depend on daily. Familiarity with API tooling - GraphQL, REST, Postman, authentication patterns (JWT, OAuth) - is a plus; deep expertise isn’t required, but you should be comfortable navigating and integrating against APIs quickly. You work fluently in LLM-based development environments like Claude Code or Codex - these are core tools in how we build, not a nice-to-have.

Customer-Facing Communication You’ve worked directly with customers in a technical capacity - solutions engineering, consulting, technical account management, or a previous FDE role. You can synthesize complex data needs for an executive audience and discuss matching architecture with a data engineering team in the same meeting. You’re comfortable navigating enterprise environments with competing stakeholders - sales operations, IT, marketing ops, finance, and revenue leadership all sit at the same table.

Go-to-Market Data Familiarity (Preferred) Experience working with B2B data, CRM systems (Salesforce, HubSpot), enrichment and orchestration tooling (RingLead, Clearbit, Demandbase), or similar go-to-market infrastructure. Familiarity with concepts like firmographic enrichment, entity resolution, hierarchy management, TAM modeling, intent data, and account-based prospecting. This isn’t required - if you’re a fast learner with the right instincts, we’ll get you there.

3.43.4 out of 5 stars Waltham, MA Remote $171,500 - $269,500 a year