Forward Deployed Data Engineer (Integration) at Hilberts

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 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. LOCATION SAN FRANCISCO, WITH OCCASIONAL TRAVEL FOR TEAM MEETS, OFFSITES OR CUSTOMER ENGAGEMENTS. COMPENSATION COMPETITIVE SALARY + EQUITY PACKAGE, COMMENSURATE WITH EXPERIENCE. PERFORMANCE-BASED BONUSES TIED TO PROJECT MILESTONES AND CUSTOMER IMPACT. THE HIRING JOURNEY SHORT FORM → INTRO CALL → TECHNICAL WORKING SESSION → TEAM CONVERSATIONS → OFFER FAST, HUMAN, NO BUREAUCRACY.