Forward Deployed Data Engineer; Integration

Hilbert is a scalable, data science-first growth engine that gives B2C teams predictive clarity into user behavior, revenue drivers, and the actions that drive sustainable growth. Fully agentic by design, Hilbert shrinks months-long decision cycles to minutes.

From Fortune 10 enterprises to beloved brands like Fresh Direct, Blank Street, and Levain Bakery, operators run their growth on Hilbert. We're also co-building alongside leading AI companies.

We’re looking for a Forward Deployed Data Engineer who can bridge the gap between our customers’ messy data ecosystems and Hilbert’s AI Growth Engine. This isn’t a "ticket-taker" role. You are the architect of the bridge. You will own the entire integration lifecycle from the first technical discovery call with a mid-market retailer to deploying custom stacks within a massive enterprise’s own infrastructure.

You’ll be the one listening to the customer, mapping their unique data schemas to our canonical models, and ensuring that when our AI/ML models "wake up," they have a clean, high-fidelity view of the business.

• The "Translator" Ability: You can speak "Engineer" and "Business" equally well. You can extract the logic of a custom dimension table from a customer who doesn't have documentation.

• Architecture Mindset: You understand the difference between a quick-and-dirty batch sync and a robust, incremental pipeline.

• Tech Proficiency: Deep experience in Python and SQL
  . You’ve ideally worked with modern orchestration (
  Dagster
  , Airflow) and ingestion tools (
  Airbyte
  , Fivetran).

• Adaptability: You are comfortable working with MongoDB and Clickhouse
  , but you don't blink if a customer asks you to deploy on their specific cloud infra.

• Availability: You are based in or aligned with US timezones and are ready to hop on a plane for an enterprise site visit when the stakes are high.

Own the technical onboarding for new customers, transforming source data into Hilbert’s canonical models.

• Design and implement incremental syncs for massive fact tables and full syncs for dimensions.

• Navigate enterprise-level complexity: custom data models, on-prem/private cloud deployments, and unique security requirements.

• Collaborate with the AI/ML team to ensure the data pipelines provide the exact context needed for agentic flows and insights.

• Build the "Last Mile":
  Making sure the deployment of your customer is successful and the portal is setup and ready to be used by them.

• Experience in E-commerce or Retail sectors (understanding what a "SKU" or "Attribution Window" is without being told).

• Experience with product event usage data.

• Working with Data Scientists or ML Engineers.

• Experience integrating B2B solutions for enterprise companies.

• Having Fullstack Software Development skills.

• Having experience with multiple different cloud infra providerse care about how you think and how you ship - not how many years are on your resume.

• You're a strong Python engineer. Your code is clean, testable, and production-ready.

• You have real experience with Lang Chain, Lang Graph, or equivalent agent/orchestration frameworks. You've built with them, hit their limits, and worked around them - not just followed tutorials.

• You communicate with clarity and conviction. You can explain a technical decision to a non-technical founder and debate architecture tradeoffs with a senior engineer . Communication is not a nice-to-have here - it's core to the role.

• You take ownership. You don't wait for tickets. You see what needs to be built, raise your hand, and ship it.

• You thrive in ambiguity. AI products evolve fast. Requirements change. You're energized by figuring it out.

• You move at startup speed. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment.

• Experience building evals pipelines — designing metrics, running systematic evaluations, and using results to drive iteration on AI systems.

• Backend software engineering experience — building APIs, services, data infrastructure, or production systems beyond the ML/AI layer.

• Exposure to retrieval-augmented generation (RAG), vector…