Data Analyst
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Summary
Fabric is hiring a Data Analyst to build dashboards, automated reporting, and strategic analyses of clinical operations and health outcomes for its healthcare platform, partnering with clinical leadership and client-facing teams. Day-to-day work is SQL-based data warehouse analysis with BI tools (Tableau/Thoughtspot), Python/R, and heavy use of AI assistants to speed up workflows.
About the Role
Fabric is looking to bring on a Data Analyst to provide customer insights and support our medical leadership through actionable reporting. As part of our analytics team, you partner with the clinical organization to deliver strategic analyses and build automated reporting around clinical operations and health outcomes. You will dive into complex datasets to analyze trends, identify opportunities, and quantify the potential value of new initiatives. Your work helps ensure our clients have clear visibility into the clinical value Fabric provides, supporting the continuous improvement of our medical practice to best serve our patients.
This role requires proven quantitative and critical thinking skills along with a solid understanding of healthcare analytics. As a vital contributor to a lean team, you clearly communicate complex findings to diverse audiences, collaborate across multiple functions, and leverage modern AI tools to accelerate data processing and generate insights.
What You'll Do
As a Data Analyst, your primary responsibilities include:
- Build and maintaining reporting that tracks physician performance, care quality, and consistency of clinical standards across the platform.
- Design dashboards and visualizations that make clinical and business metrics easy to act on for both clinical and non-clinical leaders.
- Partner closely with our client-facing organization to surface clinical insights for customers and prospects.
- Collaborate with clinical operations and physician enterprise leaders to translate open questions into clear, well-scoped analyses.
- Write and maintaining SQL for the data warehouse to transform raw clinical and operational data into analysis-ready datasets, utilizing AI coding assistants to accelerate query development.
- Collaborate with product and engineering teams to improve the quality, structure, and reliability of underlying clinical data.
- Support ongoing reporting needs to help the team move from one-off requests toward scalable, repeatable reporting.
- Leverage AI tools for advanced data cleaning, initial pattern recognition, and drafting analytical summaries to increase efficiency.
Why You Might Be a Good Fit
- You enjoy translating ambiguous clinical questions into clear, actionable data models and dashboards.
- You have a solid foundation in healthcare analytics and understand the nuances of clinical and operational data.
- You excel at bridging the gap between technical data engineering and business-facing clinical leadership.
- You are proactive in seeking out ways to scale reporting rather than manually answering the same questions repeatedly.
- You are an early adopter of technology, actively using AI tools to write queries faster and streamline your analytical workflows.
This Might Not Be The Right Fit If...
- You prefer working with perfectly clean, strictly structured data over navigating the messy reality of real-world healthcare datasets.
- You want to focus solely on building complex machine learning models rather than delivering practical, descriptive analytics and BI reporting.
- You struggle to communicate technical findings to non-technical stakeholders in a clear, digestible manner.
- You prefer traditional manual coding workflows and are hesitant to adopt AI assistants to scale your data analysis tasks.
Your Qualifications
- 2+ years of experience analyzing data in a professional setting, ideally within healthcare.
- Proven proficiency in SQL.
- Experience building dashboards or reports in a BI tool such as Tableau or Thoughtspot.
- Familiarity with Python or R for complex analyses.
- Proficiency in utilizing modern AI assistants for query generation, data cleaning, and workflow automation.
- Bachelor’s degree, preferably in a quantitative discipline; an advanced degree is a plus.
- Comfort managing multiple analysis projects at once and adapting to shifting priorities in a fast-paced environment.
Bonus Points
- Familiarity with common healthcare datasets, standards, and measures such as HEDIS.
- Experience with analytics tools such as Heap, Mixpanel, or Amplitude.
- Experience with A/B testing, including statistical hypothesis testing methodologies.
- Experience working within Databricks.