Analytics Engineer

San Francisco Bay Area (Hybrid — Burlingame office 2-3x/week required)
Must currently reside locally; this is not a remote-eligible role.

Simbe Robotics is a leading retail robotics company providing in-store intelligence solutions that help retailers optimize operations, improve shelf execution, and deliver valuable data insights. Our autonomous robots and multi-modal data collection systems are transforming how retailers manage inventory and make data-driven decisions.

Position Overview

We are looking for an experienced Analytics Engineer to join the Data Science & Analytics team, owning production-grade data pipelines from ideation through delivery. This is an engineering-forward role,  you'll partner closely with Product Management, Engineering, and Data Scientists to ship reliable, user-facing features that surface insights from our retail data at scale. Establish organized data marts to empower self-serve analytics and AI powered insights.

You are someone who thrives at the intersection of data and software engineering: you write production code, own the reliability of the systems you build, and drive cross-functional projects to completion without waiting to be unblocked.

Leveling (Senior or Staff) will be determined through the interview process based on your background and technical depth.

Key Responsibilities

• Own production pipelines end-to-end — design, build, and maintain robust analytics pipelines that run reliably in production, including monitoring, alerting, and iterative improvement

• Scope and deliver features — take raw data and shape it into analytical models via Kimball Dimensional modeling with dbt. 

• Drive cross-functional delivery — proactively identify blockers, align stakeholders across teams, and move projects forward with minimal oversight

• Apply AI tooling to accelerate work — leverage LLMs, agents, and other AI-assisted workflows to increase the speed and quality of analysis and development

• Translate retail data into decisions — connect store-level signals (inventory, on-shelf availability, task execution, etc.) to meaningful business outcomes for both internal teams and retail clients

• Raise analytical standards — establish best practices for reproducibility, documentation, and code quality across the team's data science and analytics work

• Build conversational data experiences — design and prototype AI agent or chatbot interfaces that allow internal or external users to query and explore retail data through natural language