Forward Deployed Data Engineer

About Hilbert

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.

As we expand across Latin America, we're building our first technical team in the region. We're looking for founding engineers who want to work directly with customers, solve complex data problems, and help shape how Hilbert builds and operates in LATAM.

The Mission: Architect the Ground Truth

At Hilbert, we shorten "months" to "minutes." We build the AI Growth Engine that Fortune 500 enterprises and iconic B2C brands use to navigate their revenue drivers in real time.

We are expanding our deployment model. Whether we're ingesting data into our high-performance ClickHouse managed warehouse or implementing Warehouse-Native logic directly within a customer's own data warehouse, such as Snowflake or BigQuery, the goal is the same: give our AI agents a perfect, high-fidelity view of the business.

As a Founding Forward Deployed Data Engineer in LATAM, you'll be at the center of bringing Hilbert to new enterprise customers across the region. You'll work directly with their data and technical teams, architect deployments, solve messy integration problems, and turn what you learn in the field into better infrastructure and product.

The Hard Problem: Architectural Hybridity & Semantic Clarity

The challenge isn't just moving rows; it's ensuring reasoning consistency. You're responsible for making sure our AI produces the same high-quality insights regardless of where the data resides.

Our Current Hurdles

• The Dual-Track Challenge: We're building a system that can either pipe data into our ClickHouse stack using Dagster and Airbyte, or execute "Zero-Extract" transformations natively in the customer's warehouse. You'll need to master both worlds.

• The AI Semantic Layer: For customers with custom datasets that don't fit our canonical models, you'll build the "Translator" — defining metadata and business logic so our agentic flows can understand custom data without hallucination.

• We're building a to automate the messy parts of integration. You'll prompt and partner with this agent to analyze customer data, suggest mapping alternatives, and generate the first version of pipelines automatically, dramatically reducing integration time.