Analytics Engineer (Member of Data Staff)

Join our team as an Analytics Engineer, where you'll play a crucial role in building the foundation for an AI-native data organization. You'll design core data models, pipelines, and governance practices that power the entire company. Your work will help teams make strategic decisions, operate the business, and move faster with trusted data. You'll also ensure that our systems are secure, privacy-aware, and accessible to AI agents, data scientists, and the rest of the company.

• Construire la base de données principale - concevoir et maintenir des modèles de données, des marts et des pipelines de haute qualité qui rendent l'analyse rapide, fiable et réutilisable.
• Gérer l'entrepôt de données - aider à posséder l'architecture de l'entrepôt, les environnements, les autorisations, les performances, les coûts, le cycle de vie des données et l'hygiène opérationnelle.
• Automatiser la qualité des données et la maintenance - construire des workflows assistés par l'IA qui détectent les problèmes, expliquent les causes profondes, suggèrent des corrections, génèrent des tests et réduisent les interventions manuelles.
• This role is for someone who can operate at the boundary of analytics engineering, data engineering, data governance, and internal product. You care about dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and the details that make data trustworthy. You also believe AI should make the data stack faster, easier to maintain, and more accessible across the company without weakening security or governance
• Deep SQL expertise - you can reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries
• Operational judgment - you care about reliability, governance, security, cost, and long-term maintainability
• Autonomy and execution - you can take projects from ambiguous problem to production-quality system with minimal oversight
• 6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role
• Pipeline ownership - you've built, maintained, debugged, and improved production data pipelines
• AI-native working style - you already use AI to speed up development, documentation, QA, exploration, and repetitive workflow automation
• Stakeholder fluency - you know how to turn messy analytical requirements into trusted models, metrics, and reusable data assets
• Strong data modeling experience - you've worked hands-on with dbt (or a similar transformation framework) in production, and you understand dimensional modeling, data contracts, testing, and how analytical schemas should evolve
• Warehouse management experience - you've worked with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership
• Governance mindset - you think clearly about data ownership, access controls, privacy, retention, lineage, auditability, and the risks of making data too easy to access
• Snowflake administration, optimization, cost management, or warehouse performance tuning
• Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews
• Experience with Databricks or other modern data infrastructure
• Experience building semantic layers, metrics layers, metadata systems, or data catalogs
• Python experience for data tooling, automation, orchestration, or quality checks
• Previous experience as an early analytics engineer or data engineer at a high-growth startup