Analytics Engineer - Seattle, WA

Otter•Seattle, WA

2d•$137,000 - $162,000•Onsite

About The Position

In the past, to be a successful restaurateur, you simply had to have a passion for food and a passion for people - but to succeed as a digital restaurateur you also need to have a passion for technology. We believe in the joy of serving others, and that's why we created Otter – to help restaurateurs succeed in online food delivery. Restaurants around the world, both large and small, including Chick-fil-A, Ben & Jerry’s, KFC, and Eataly trust our software to power their delivery business. We increase sales, reduce order issues, and decrease delivery headaches. The restaurant industry has zero data standardization - POS systems, delivery apps, and inventory tools all speak different languages. This causes semantic drift, impedes AI enablement, and erodes trust. The Analytics Engineering team is tackling this head-on through best-in-class data normalization and modeling, a semantic layer, and automated data quality frameworks - contributing to a world-class data ecosystem from the ground up. As an early and key contributor to this team, you'll balance hands-on technical ownership with deep cross-functional collaboration. That means building scalable data models and pipelines while partnering with Product, Engineering, Operations, and Data Science to understand how restaurants use Otter, measure platform quality and growth, and transform raw operational data into production-ready datasets that power business decisions and AI-driven products.

Requirements

5+ years in the data space as an analytics engineer or data engineer, preferably in a scaling startup environment.
Strong technical foundations with deep experience in SQL, Python, version-controlled environments, and modern data tooling, including hands-on experience with a SQL-based transformation tool (e.g., dbt, SQLMesh, or similar).
Significant experience with a large-scale (1B+ rows) data ecosystem, with familiarity across both batch processing and real-time or OLAP query engines.
Strong understanding of data modeling concepts: star schemas, slowly changing dimensions, incremental materialization strategies.
Experience partnering with Product, Engineering, and Data Science to deliver data products.
Demonstrated ability to balance strategic thinking with hands-on technical ownership.
A "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end.
A passion for high-quality, scalable, and highly usable technical systems.

Responsibilities

Design, build, and maintain scalable data pipelines and models, owning the full lifecycle from source system ingestion through canonical datasets to production-ready analytics.
Build and optimize data models that run against both batch and real-time query engines, understanding the tradeoffs and appropriate use cases for each.
Partner with Engineering, Product, and Data Science teams to understand data needs and data generation patterns, define source-of-truth datasets, and ensure data quality across the pipeline.
Design and maintain data integrity standards, SLAs, alerting, and best-practice data validation frameworks.
Drive the development of self-service analytics solutions and AI-powered data tools to scale data access for both internal teams and external customers.
Contribute to the design and evolution of our semantic layer and data modeling standards across the organization.
Foster a culture of technical excellence, continuous learning, and data-fluency.