Staff/Senior Analytics Engineer, Data Science & Analytics (DSA)

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 (nice to have)

Qualifications

• 5+ years of experience in analytics engineering or a closely related role, with demonstrable delivery of production features

• Experience with dbt for data transformation and Kimball Dimensional modeling: writing models, tests, and documentation as part of a production analytics engineering workflow

• Solid SQL and experience working with large-scale cloud data platforms (GCP/BigQuery preferred)

• Experience owning the full lifecycle of analytics features: scoping, building, shipping, and maintaining

• Proven ability to work across functions: you've partnered with Engineering, Product, or Commercial teams and know how to communicate tradeoffs and drive alignment

• Retail industry experience strongly preferred (store operations, inventory, merchandising, supply chain, or equivalent)

• Hands-on experience using AI tools (LLM APIs, coding assistants, prompt engineering) to accelerate analytical work

Preferred Qualifications

• Familiarity with, pipeline orchestration (Airflow or similar), model monitoring, CI/CD for analytical workflows

• Experience with data visualization tools (Looker, Tableau, or similar) for communicating findings to non-technical stakeholders

Why You'll Love Working with Us

• Ownership that matters - you'll have real scope over systems and features that run in production and directly affect how our retail partners operate

• High-signal environment - focused team where your work is visible and your technical judgment is trusted

• Retail at scale - Simbe's data spans thousands of stores and billions of shelf observations, a genuinely rich and challenging domain