Senior Data Analyst

Join our Data & Analytics team as a Senior Data Analyst, where you will be the analytics partner to our Customer Success organization. You will own the reporting lifecycle, build data models and dashboards, and actively leverage AI tools in your daily analytics work. Your role will involve collaborating closely with the Customer Success team, designing and building production-quality LookML models, writing complex SQL queries, and identifying AI-powered use cases within the analytics workflow. You will also explore data to surface trends and opportunities, define data quality metrics, and contribute to the evolution of our BI strategy.

• Assurer que les clients reçoivent des rapports de haute qualité, des revues d'affaires trimestrielles aux analyses ad hoc.
• Posséder le cycle de vie des rapports de bout en bout, y compris la construction de modèles de données et de tableaux de bord.
• Utiliser activement les outils d'IA dans le travail analytique quotidien et identifier les opportunités d'amélioration.
• We are looking for someone who does not wait to be told what to analyze. You see a pattern, dig in, form a hypothesis, and bring insights to the table before anyone asks. A strong sense of business context and the curiosity to understand why numbers move is just as important as the technical skills to query them
• Deep expertise in Looker, including strong command of LookML: building and maintaining models, views, explores, derived tables, and parameterized dashboards. This is the primary BI tool for the role
• Excellent communication and presentation skills, with the ability to tailor the depth and framing of analysis to different audiences (Customer Success, clients, leadership)
• Experience building and delivering recurring client or stakeholder reports (e.g., QBRs, executive dashboards, performance reviews)
• Strong Python skills for data analysis, scripting, and automation. You should be able to write clean, well-structured Python to query databases, transform data, build automated reports, and support analytical workflows
• Strong business acumen and the ability to understand the "so what" behind the numbers. You can connect data patterns to business outcomes and communicate findings to non-technical stakeholders
• Strong appetite for integrating AI into analytics workflows. You actively look for opportunities where AI can automate, enhance, or transform how data is analyzed and delivered, and you are comfortable experimenting with new tools and approaches
• Familiarity with cloud data warehouses (Redshift preferred) and data lake concepts (S3, Parquet)
• Proficiency with AI-assisted development and analytics tools (e.g., Claude, ChatGPT, GitHub Copilot, Cursor). You should already be using AI to write code faster, explore data more efficiently, and improve the quality of your analytical output
• Expert-level SQL: complex joins, window functions, CTEs, subqueries, and query performance tuning. You should be comfortable writing SQL as your primary analytical language
• 5+ years of professional experience in a data analyst, analytics engineer, or BI-focused role
• Self-starter mentality: you proactively identify problems, dig into data without being asked, form hypotheses, and bring insights to stakeholders with actionable recommendations
• Experience in health tech, digital health, or healthcare services, including familiarity with healthcare-specific data (claims, clinical, eligibility, pharmacy, outcomes measurement)
• Hands-on experience building or deploying AI-powered analytics features, such as automated reporting narratives, anomaly detection, classification models, or LLM-based data summarization
• Familiarity with AWS data services (Glue, Athena, S3, CloudWatch)
• Experience with event-based analytics platforms such as Mixpanel or Amplitude
• Exposure to dbt or similar analytics engineering tools for managing transformation logic
• Familiarity with version control (Git) for LookML and analytical code
• Prior experience working closely with Customer Success, Account Management, or client-facing teams
• Background in a startup or high-growth environment where you wore multiple hats and owned outcomes end to end