Senior Forward Deployed Engineer

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.

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.

You'll work with your manager and more senior FDEs to diagnose which the customer actually needs, scope the work, and deliver — taking on increasing ownership of that diagnosis as you build track record.

Own Strategic Customer Engagements End-to-End Serve as a primary technical point of contact for assigned strategic accounts.

Run discovery sessions with data engineers, sales operations leaders, and revenue stakeholders.

Help 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 customer stakeholders.

Synthesize complex go-to-market data needs into clear, actionable proposals — then deliver the solution.

You're comfortable whiteboarding matching architecture with a data engineering team and walking a sales operations director through disposition codes, and you're developing the ability to present ROI and strategy to executive audiences.

Contribute to the FDE Playbook Document what works: discovery frameworks, engagement phases, integration patterns, deliverable templates, success metrics.

The current strategic-account engagements are reference patterns — help extract what's repeatable from each new account and feed it into a model that scales to additional strategic accounts, verticals, and eventually a larger 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.

Help identify expansion opportunities as they emerge — new use cases, new personas, new datasets, displacement opportunities against incumbent providers.

High Ownership, Comfort with Ambiguity This function is still being defined at ZoomInfo.

You take ownership of your workstreams, are comfortable making judgment calls with incomplete information, and can operate effectively even where process doesn't yet exist.

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-style role.

You can synthesize complex data needs for a business audience and discuss matching architecture with a data engineering team in the same week, if not 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.

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.

Why This Role The mandate is clear.

Driving data consumption and growth across ZoomInfo's strategic accounts is a top company priority.

We already have working prototypes, validated customer demand, and executive sponsorship.

The data team, product team, and infrastructure are in place.

What's needed is someone ready to execute and grow with the function.

Enterprise customer access.

ZoomInfo's customer base includes market-leading GTM organizations at some of the largest enterprises in the world.

You'll be embedded with these teams, solving real problems with meaningful budgets and complex data needs.

Best-in-class data and infrastructure.

ZoomInfo's data foundation, GTM Data Store, query infrastructure, and vertical dataset catalog give you the raw materials to build custom intelligence applications that aren't possible anywhere else.

#LI-AR2 #LI-remote

Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training.

Your recruiter can share more information about the specific salary range for your desired work location during the hiring process.

We want our employees and their families to thrive.

In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being.

Learn more about ZoomInfo benefits here.

ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation.

Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.

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