Data Analyst

📋 Description

• • Drive operational excellence by proactively identifying bottlenecks and inefficiencies in healthcare advocate matching and workflow processes, transforming complex datasets into actionable insights that directly improve business performance.
• • Design, implement, and maintain scalable analytics systems and automated dashboards to replace manual reporting, ensuring stakeholders have self-serve access to key metrics critical to marketplace operations.
• • Collaborate with engineering teams to maintain and refine clean, reliable data models and SQL transformations that scale with company growth, ensuring data integrity across the organization.
• • Synthesize technical findings into clear, concise, and compelling narratives for non-technical audiences including product managers, operations teams, and executives, explaining trends, root causes, and recommended actions.
• • Wrangle and analyze structured and semi-structured data, including nested JSON datasets, to extract meaningful signals from noisy, real-world healthcare and marketplace data sources.
• • Serve as a strategic data partner to the business by answering high-impact questions about supply-demand dynamics, match quality, advocate performance, and patient outcomes in a two-sided healthcare marketplace.
• • Build and optimize tracking systems that measure the effectiveness of operational workflows and customer journeys, enabling data-driven decisions on resource allocation and process improvements.
• • Translate business objectives into data requirements, defining KPIs and success metrics that align with company goals and scaling initiatives.
• • Maintain and enhance data documentation, ensuring transparency and consistency in how metrics are calculated and interpreted across teams.
• • Prioritize speed and impact over perfection, delivering timely insights even under ambiguity and shifting priorities typical of a high-growth startup environment.
• • Contribute to the evolution of the data infrastructure by advocating for improvements in data quality, pipeline reliability, and analytical tooling.
• • Support cross-functional teams with ad-hoc analyses to address urgent business questions, often working across product, operations, and customer success functions.
• • Ensure data storytelling is grounded in business context, always connecting analysis to tangible outcomes such as reduced wait times, improved match rates, or increased patient satisfaction.
• • Stay current with evolving data practices and tools, proactively recommending enhancements to the analytics stack that improve efficiency or insight depth.
• • Maintain strict adherence to data governance and privacy standards when handling sensitive healthcare-related data.
• • Actively participate in team rituals including data reviews, sprint planning, and retrospectives to ensure alignment between analytics and business priorities.
• • Champion a culture of data literacy by helping non-analysts understand how to interpret and use key metrics in their daily work.
• • Balance long-term data architecture goals with immediate business needs, delivering both quick wins and sustainable systems.
• • Work closely with product and engineering to ensure data collection aligns with product features and business objectives from inception.
• • Monitor data quality metrics and flag anomalies or inconsistencies that could impact decision-making or customer experience.
• • Continuously seek opportunities to automate repetitive reporting tasks and reduce manual effort across the organization.
• • Use data to uncover hidden opportunities for improving advocate-patient matching efficiency and overall platform effectiveness.
• • Maintain a relentless focus on user impact, ensuring all analyses ultimately contribute to better health outcomes for patients.