Senior Data Analyst

9amHealth

4d•Remote

About The Position

9amHealth is seeking a Data Analyst to join their Data & Analytics team. This role will serve as the analytics partner for the Customer Success organization, responsible for providing clients (large employers and health plans) with top-tier reporting, including recurring Quarterly Business Reviews and ad hoc analyses. The analyst will own the entire reporting lifecycle, from building data models and dashboards in Looker to writing Python and SQL code. A key aspect of this role is proactively identifying trends, forming hypotheses, and delivering insights. The position also emphasizes the active use and exploration of AI tools to enhance daily analytics work, reporting, and client deliverables, viewing AI as a multiplier for the data team's capabilities.

Requirements

5+ years of professional experience in a data analyst, analytics engineer, or BI-focused role.
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.
Expert-level SQL: complex joins, window functions, CTEs, subqueries, and query performance tuning. You should be comfortable writing SQL as your primary analytical language.
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.
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.
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.
Self-starter mentality: you proactively identify problems, dig into data without being asked, form hypotheses, and bring insights to stakeholders with actionable recommendations.
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.
Experience building and delivering recurring client or stakeholder reports (e.g., QBRs, executive dashboards, performance reviews).
Excellent communication and presentation skills, with the ability to tailor the depth and framing of analysis to different audiences (Customer Success, clients, leadership).
Familiarity with cloud data warehouses (Redshift preferred) and data lake concepts (S3, Parquet).

Nice To Haves

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.

Responsibilities

Own client-facing analytics: build, maintain, and continuously improve the dashboards and reports used in Quarterly Business Reviews and ad hoc client requests.
Partner closely with the Customer Success team to understand client needs, translate business questions into analytical frameworks, and deliver insights that strengthen client relationships.
Design and build production-quality LookML models, explores, and dashboards in Looker, serving as the team's Looker subject matter expert.
Write complex SQL queries across Redshift, Aurora/MySQL, and Athena to extract, transform, and analyze data at scale.
Develop Python scripts and notebooks for data wrangling, automated reporting, and analytical workflows that go beyond what SQL alone can do.
Leverage AI tools (e.g., Claude, GitHub Copilot, Cursor) in day-to-day analytics work to accelerate query development, code generation, data exploration, and documentation.
Identify and champion AI-powered use cases within the analytics workflow, such as automated anomaly detection, natural-language data summarization, predictive insights for client reporting, or AI-assisted QBR narrative generation.
Proactively explore data to surface trends, anomalies, and opportunities, and present findings with clear business context and actionable recommendations.
Define and monitor data quality metrics for client-facing reporting, ensuring accuracy, consistency, and timeliness.
Collaborate with the Data Engineering team to define data model requirements, validate pipeline outputs, and improve the analytical data layer.
Document reporting logic, data definitions, and analytical methodologies to enable self-service and knowledge sharing across teams.
Contribute to the evolution of our BI strategy, including dashboard governance, naming conventions, and version control practices for LookML.