Staff/Senior Analytics Engineer, Simbe

• 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
• 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

Desirable

• 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
• 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
• 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)