Forward Deployed Data Engineer

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 underlying problems, scope use cases, design solutions, build applications, deploy in customer environments, and stay accountable for outcomes.
• Bridge Technical and Business Audiences: Present to executive leadership. Synthesize complex go-to-market data needs into actionable proposals. Comfortable whiteboarding matching architecture with data engineering teams, walking sales ops through disposition codes, and presenting ROI to a CRO.
• Build the FDE Playbook: Document discovery frameworks, engagement phases, integration patterns, deliverable templates, and success metrics. Extract repeatable patterns from engagements and turn them into a scalable model. Feed field learnings back to product, engineering, and data teams.
• Drive Stickiness and Expansion: Embed applications that create stickiness and surface expansion opportunities.

What Engagements Typically Look Like

• Entity resolution at scale — reconciling legal entity hierarchies with customer GTM records
• Hierarchy management — enforcing one-to-one matching across regions, fixing parent-child linkages, dispositioning orphaned accounts
• Location-level precision — moving customers from monolithic HQ-level enrichment to geo-based firmographics
• Automated, no-human-in-the-loop logic — entity suppression, disposition-based matching, orchestration rules
• Data warehouse as the operating layer — moving analysis out of CRM into Snowflake or BigQuery
• Buying group filtering — applying persona-density criteria across hierarchies

What You'll Build

Three Pillars:

• Data Foundation — golden reference matching, persistent IDs, unified entity profiles
• Data Management — business-specific logic, customer definitions, account models, entity resolution
• Activation — TAM to SAM to SOM, fit scoring, in-market signals

Five Capability Areas:

• Data Foundation Development — Match records across CRM, ERP, billing, and marketing systems to golden reference datasets; build custom disposition logic, domain validation, marketability classification
• Account Architecture & Entity Resolution — Define account structures and build automated logic for duplicate resolution, inactive entity disposition, hierarchy linkages
• TAM Development & White Space Discovery — Build complete addressable market against ICP criteria, suppress existing customers, surface white space, apply buying group filters
• Account Fit Scoring & In-Market Signals — Build custom fit models from win/loss patterns; configure evergreen and tailored intent signals
• Ongoing Governance & Automation — Match orchestration rules, enrichment segmentation, CRM field locking, warehouse integration (Snowflake, BigQuery)

Requirements

• Experience with data engineering, applied product development, and stakeholder management
• Strong skills in entity resolution, hierarchy management, and data warehouse technologies
• Ability to work directly with strategic enterprise accounts
• Comfortable bridging technical and business audiences
• Experience with Snowflake, BigQuery, or similar data platforms
• Background in building automated matching and data quality pipelines