Forward Deployed Data Engineer (Integration)
Hilbert's AIHilbert 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 FreshDirect, 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 ROLE
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.
WHO THRIVES IN THIS ROLE
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.
Bonus Points
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.
The profile:
You're a strong Python engineer. Your code is clean, testable, and production-ready.
You have real experience with LangChain, LangGraph, 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
Strong pluses:
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 databases, or LLM-powered search and recommendation systems
Experience at early-stage startups or high-growth environments where you wore multiple hats
You might be:
A backend engineer who went deep on LLMs and never looked back. An ML engineer who realized they love building products, not just models. A startup CTO who wants to go deep on AI at a company where the stack is the product. Someone who's been hacking on agents and pipelines nights and weekends and wants to do it full-time with real enterprise stakes. What matters: you ship, you own it, and you communicate like a teammate — not a silo.