Staff Analytics Engineer

About the role

Join our team as a Staff Analytics Engineer, where you will own the data and reporting foundation for Accounting and ensure the operational reliability of the pipelines that power reporting, reconciliations, and automation. You will build and scale a single source of truth for financial information using dbt and Snowflake, and directly manage and mentor one Analytics Engineer. This role offers high autonomy and significant cross-functional partnership across various teams. Key missions: Construire et posséder des modèles dbt pour la plateforme de sous-livre financier, y compris les conventions de nommage, les macros et les modèles réutilisables.. Mettre en œuvre des contrôles de qualité des données solides dans dbt : tests (unitaires/relationnels/affirmations), fraîcheur, vérifications d'anomalies et rapprochements automatisés.. Collaborer avec les équipes d'ingénierie en amont pour définir les entrées et améliorer la qualité des données sources via des contrats et la gestion des changements. Profile: - Strong SQL and data modeling skills; experience building canonical datasets that support finance reporting, reconciliations, or other correctness-critical outcomes - Experience operating data pipelines in a warehouse environment (Snowflake preferred): performance tuning, cost awareness, and reliability practices - Deep, hands-on analytics engineering experience: you have built and maintained production dbt projects (models, tests, macros, documentation, CI) and can own them end-to-end - Deep Snowflake technical expertise: architecture patterns (micro-partitioning/clustering, query optimization), security/governance (RBAC, masking policies), and operational excellence (monitoring, cost management, reliability) - Strong ownership mindset and ability to lead ambiguous work through influence across Finance, Accounting, and Engineering stakeholders - Strong programming skills (Python preferred) for building data tooling and automation (e.g., control checks, reconciliation workflows, utilities, CLIs, and integrations) - Experience with accounting concepts (subledgers, journal entries/posting logic, balance rollforwards, tie-outs, close processes) - Experience designing SOX-friendly controls and producing repeatable evidence from data pipelines - Experience applying AI/LLMs to data quality or reconciliation workflows (e.g., anomaly explanation, automated investigation summaries), with strong governance and human-in-the-loop review - AWS experience (nice to have): S3/IAM, data platform primitives, and cloud architecture concepts that support secure, reliable data products