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

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)

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

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

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