Senior Analytics Engineer

Join Material Bank as a Senior Analytics Engineer, where you will own the analytics engineering for key business areas, design and maintain production-quality dbt models in Snowflake, build high-value dashboards in Tableau, and automate recurring analytics work. This hands-on technical role requires strong experience in analytics engineering, data analytics, and SQL, as well as a passion for ownership and attention to detail.

Assumer la responsabilité de l'ingénierie analytique pour des domaines d'affaires clés, en traduisant les définitions de métriques en modèles de données, tableaux de bord et insights.

Python for automation and data validation

About Material Bank

Appetite for hard work. We are a small, ambitious team with a lot of goals. The work is demanding and the hours vary with what needs to get done. We are upfront about that because it is what the role is

Responsibilities

• Concevoir, construire et maintenir des modèles dbt de qualité de production dans Snowflake, transformant les définitions de métriques approuvées en modèles fiables et réutilisables.
• Collaborer avec les analystes et les parties prenantes pour interpréter les données, construire le contexte et faire remonter les risques avant qu'ils ne soient posés.

Requirements

• Precision and attention to detail. You understand the nuance behind a number, verify before you publish, and produce work that leaders and brand partners can trust without checking
• Ownership, end to end. You own what you build, from the first stakeholder conversation to the model, the dashboard, and the fix when something breaks
• Strong hands-on Snowflake and dbt (or a comparable modern data stack), including dimensional modeling, incremental models, testing, and documentation
• Strong Tableau skills. Sigma, Looker, or similar a plus
• 5+ years in analytics engineering, data analytics, or a similar technical analytics role, owning data models and reporting that a business relies on
• Strong judgment on metric design: grain, denominators, filters, time zones, deduplication, and the ways a correct number can still mislead
• Clear, concise communication and the ability to explain technical concepts to non-technical audiences
• An automation mindset. When you see something repetitive or fragile, your instinct is to find a better way to build it
• Comfort working across several domains at once, prioritizing your own work, and managing stakeholders directly
• Expert SQL on large and complex datasets. You write clean, efficient queries and can debug someone else's logic as easily as your own
• Commercial sense and curiosity. You want to understand why a number matters, can explain what a change means for the business, and hold a position under questioning
• Catalog, product attribute, or search data
• Experimentation and causal methods: A/B testing, cohort analysis, difference-in-differences
• Event and behavioral data (Segment, GA4, or similar)
• Marketplace, e-commerce, or B2B SaaS analytics

Preferred

• Embedded or customer-facing analytics (ClickHouse, Preset, or similar)
• Semantic layers and AI-assisted analytics (Snowflake Cortex, dbt Semantic Layer, or similar)