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Material Bank is the world's largest material marketplace for the architecture and design industry, operating in 37 countries and connecting thousands of designers with tens of thousands of materials from leading brands. The company is rapidly expanding into Europe with regional headquarters in Paris and offices in London and Stuttgart.
You will join the Analytics & Insights team, which owns Material Bank's analytics data layer, internal reporting, and data products that support internal teams and brand partners. This is a hands-on technical role focused on owning how analytics get built end-to-end.
In this role, you will take approved metric definitions and business questions and translate them into data models and metric logic in Snowflake and dbt, build dashboards in Tableau and Sigma, and contribute to the semantic layer that powers reporting, self-serve analytics, automation, and AI applications. You will own analytics engineering for assigned business domains, designing and maintaining production-quality dbt models that turn metric definitions into reliable, reusable models. You will identify and resolve technical inconsistencies in metric logic to ensure the business runs on one certified source of truth. You will build high-value dashboards and contribute to the semantic layer so certified metrics are reusable across BI tools, Snowflake Cortex, and embedded analytics for brand partners.
The work varies day to day as you collaborate across multiple departments and data products. You will work with stakeholders from initial conversations through model design, dashboard delivery, and ongoing maintenance. The team is small and ambitious with demanding work; you should expect variable hours aligned with business needs.
Key attributes required: end-to-end ownership from stakeholder conversation through model, dashboard, and production support; precision and attention to detail in verifying numbers before publishing; and an appetite for hard work in a fast-moving, goal-driven environment.
Requirements: The posting does not explicitly state years of experience or formal education requirements. However, the role expects strong technical proficiency with dbt, Snowflake, and Tableau; experience building and maintaining production analytics systems; ability to translate business requirements into data models; and demonstrated ownership of analytics projects from conception to delivery.
