Data Engineer

Guac

New York (NY)

On-site

USD 150,000 - 250,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Healthcare (employer-paid)

Unlimited vacation

Office food

Equinox membership

Job summary

Guac in NYC is building AI-powered forecasting to reduce grocery waste. As a Data Engineer, you will own data infrastructure, ingest billions of rows from grocers, and turn data into forecast-ready insights.

This role emphasizes scalable pipelines, collaboration with customer tech teams, and ML pipeline optimization. In-person, on-site, with strong compensation and benefits.

Qualifications

3+ years of data engineering experience.
Proficiency in Python and SQL.
Experience designing ETL systems across large datasets (Dagster or Airflow).
Ability to operate with ambiguity and deliver results.

Responsibilities

Design and build ETL pipelines processing billions of rows daily across customers using Python, Dagster, and Pub/Sub.
Model new datasets and own the data layer for new deployments—from raw integration to forecast-ready.
Optimize ML pipelines for faster, cheaper, more accurate forecasting at scale.
Collaborate with customers' tech teams to understand data systems and business logic.

Skills

Python

SQL

ETL design

Distributed data processing

Tools

Dagster

Airflow

PySpark

Dask

BigQuery

Postgres

Pandas

Docker

Job description

At Guac, we're solving grocery food waste with AI. We forecast exactly how much of each product will sell, helping grocery retailers order and produce the perfect amount of inventory — and we're building AI‑native tools that put those forecasts directly into the hands of store operators and buyers.

The grocery industry is enormous (it accounts for 4% of GDP) — and grocery food waste is a huge cost to grocers' bottom lines, but also to our planet.

Today, we're working with major supermarket chains in the US and Canada, and we've scaled to 7‑figures in ARR. We're backed by leading investors including Y Combinator, 1984 Ventures, Collaborative Fund, and angels from Open AI, Instacart, and Citadel Securities.

We've brought together an exceptional team from Palantir, BCG, Oxford, Cambridge, and MIT to solve intellectually challenging problems and tackle food insecurity and waste with technology.

We're looking for talented data engineers in NYC to join our mission.

About the Role

As a Data Engineer at Guac, you'll own the data infrastructure that powers our forecasts — the pipelines that ingest billions of rows of transaction, inventory, and operational data from grocers across the continent, and the systems that turn that data into accurate predictions multiple times a day.

You'll shape how we model new customers' data, build pipelines that scale across chains with hundreds of stores, and work on our ML systems to make them faster and more accurate. You'll occasionally work directly with customers' technical teams to understand their data and business logic — but the bulk of your time is on engineering.

Your responsibilities will include:

Data & Pipelines

Design and build ETL pipelines that process billions of rows of data multiple times per day across customers, using Python, Dagster, and Pub/Sub
Model new customer datasets and own the data layer for new deployments — from raw integration to forecast‑ready
Optimize our ML pipelines for demand forecasting — making them faster, cheaper, and more accurate at scale
Partner with customers' technical teams to understand their data systems and business logic, and translate that into our pipelines

Backend

Contribute to backend services (Python/FastAPI) that power our ordering and production planning products
Build internal tools and APIs that expose forecasts and data to our application layer
Expose our data and systems to LLMs via MCP servers, tool‑use APIs, and similar protocols

About You

3+ years of relevant data engineering experience
Strong proficiency in Python (Pandas, etc.) and SQL
Proven experience designing and implementing ETL systems across large distributed datasets, using orchestration tools like Dagster or Airflow
Comfortable operating with ambiguity and minimal process — you thrive when given a problem and trusted to figure out the solution
AI‑native: you use Claude Code, Cursor, or similar AI coding tools daily and ship significantly faster because of it
(Bonus) Experience optimizing ML pipelines or working closely with ML/forecasting systems
(Bonus) Experience with distributed computing frameworks like PySpark or Dask

What We Offer