Founding Forward Deployed Engineer

Business Needs Inc.; New York (NY); On-site; USD 110,000 - 180,000; Full time

Job summary

Business Needs Inc. in New York City seeks a Founding Forward Deployed Engineer to own end-to-end technical deployments of our revenue platform into enterprise environments.

You will bridge messy data warehouses with production-ready agent workflows, translating tribal business knowledge into validated data models and driving measurable revenue outcomes. As a foundational member of a small team, you will work directly with customers to scope data access, map schemas, and deploy agent pipelines

Qualifications

• End-to-end deployments in a technical role.
• Shipped production data pipelines independently.
• Deep SQL and data warehouse fluency.

Responsibilities

• Onboard customers end-to-end: scope data access and map schemas to business concepts.
• Build and validate semantic/ontology layer with stakeholders and translate to SQL queries.
• Deploy platform workflows and agent pipelines into customer environments and monitor outcomes.
• Manage 2–3 concurrent engagements with customer data and engineering teams.
• Provide feedback to engineering on eval guardrails and agent harness.

Skills

SQL

Data wrangling

Data warehouse fluency

AI tooling

Stakeholder management

Education

BS in CS/Data Science/Statistics

Tools

Claude

Cursor

Job description

We are looking for a Founding Forward Deployed Engineer with 2 5 years of experience to own the end-to-end technical deployment of powered revenue platform into enterprise customer environments. You'll be the bridge between messy customer data warehouses and production-ready agent workflows translating tribal business knowledge into verified data models that power autonomous revenue expansion. This is a foundational hire on a 9-person team that’s already gone from 0 to $1M in contracts with backing from TQ Ventures and HubSpot Ventures.

What will you be doing?

• Onboard new customers end-to-end: scope data access, explore undocumented warehouses, and map chaotic schemas onto real business concepts (churn, expansion, ARR)
• Build and validate the semantic/ontology layer interviewing customer stakeholders, resolving conflicting business definitions, and encoding them into verified SQL queries
• Deploy Sylvan platform workflows and agent pipelines into customer instances, then monitor and ensure they deliver measurable revenue outcomes
• Manage 2 3 concurrent customer engagements, working directly in shared Slack channels with customer data and engineering teams (~20 30% customer-facing, ~70 80% heads-down data work)
• Dogfood the internal agent tooling and provide feedback to the engineering team on how to improve eval guardrails, validation frameworks, and the agent harness itself

Dealbreaker

Work experience

Owned end- to- end customer deployments in a technical role data access, schema mapping, stakeholder management, and proving value delivery

Shipped production data pipelines or systems independently wrote code, diagnosed issues, and deployed without hand- holding

Hard skills

Deep SQL and data warehouse fluency can navigate undocumented, legacy schemas and produce validated output independently

AI- native actively uses AI coding tools (e. g. Claude, Cursor) and has built or customized their own agent- driven workflows

Miscellaneous

Must be based in or willing to relocate to New York City; fully on- site 5 days/week

Baseline

Seniority

2 - 5 years of experience as a forward deployed engineer or customer- facing software engineer

Work experience

Archetype 1: FDE, customer engineer, or solutions engineer at a data/AI company (e. g. Palantir, Databricks, Snowflake, Fivetran). Archetype 2: Analytics engineer or data scientist who moved into customer- facing delivery. Archetype 3: Ex- consulting/PE/IB with a real CS background and engineering skills (e. g. BCG, Bain tech deployments)

Education

BS in CS, Data Science, Statistics, or quantitative STEM field Strong school pedigree OR a recognizable company name on resume at least one of the two

Hard skills

Applied data science literacy distributions, seasonality, validation design; can spot when a result is noise

Soft skills

Defends technical positions under customer pushback changes mind for evidence, not authority

Miscellaneous

High tolerance for detail- oriented, repetitive data verification work (80%+ of role is data wrangling)

Nice-to-have

Work experience

Early- stage startup experience (seed Series B); Big Tech acceptable only if paired with startup or founder experience