Job description

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 At Simbe, you will be at the forefront of retail innovation, working with cutting-edge AI and robotics technologies to transform retail operations. Our culture is dynamic, inclusive, and driven by a passion for improving the way retailers operate and serve their customers. Join us to be a part of a team that is not only reshaping the future of retail but also offering immense value to our clients worldwide. Simbe Values: R. E. T. A. I. L. Result Driven - We are customer-centric and results-driven. We strive to create immense value for our team, partners, customers, and investors. Empathetic - We are sensitive and mindful. We support each other in challenging times, both professionally and personally. Transparent - We highly value open communication internally, and with our partners and customers. We are receptive to feedback. Agile - We are agile and always eager to learn. We quickly adapt to changes and customer needs. Innovative - We are bold and innovative, with an intense focus on product design and user experience. Leaders - We strive for excellence. We are accountable, the best at what we do, and leaders in our field.

Requirements

Must-haves

San Francisco Bay Area (Hybrid — Burlingame office 2-3x/week required)
Must currently reside locally; this is not a remote-eligible role.
Position Overview
Leveling (Senior or Staff) will be determined through the interview process based on your background and technical depth.
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
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

Nice-to-haves

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)
Solid SQL and experience working with large-scale cloud data platforms (GCP/BigQuery preferred)
Retail industry experience strongly preferred (store operations, inventory, merchandising, supply chain, or equivalent)
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
Simbe Values: R. E. T. A. I. L.
Result Driven - We are customer-centric and results-driven. We strive to create immense value for our team, partners, customers, and investors.
Empathetic - We are sensitive and mindful. We support each other in challenging times, both professionally and personally.

Application prep

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