Senior Analytics Engineer

Job Summary

We are seeking an experienced and visionary Senior Analytics Engineer to design, build, and own end-to-end data solutions for our Core products, spanning data ingestion, modeling, semantic layer development, and final user experience.

In this role, you will help establish and scale analytics foundations by partnering on and implementing data pipelines that integrate data from internal and external business systems while developing governed, analytics-ready models and semantic layers that power trusted metrics and insights. This position offers an exciting opportunity to contribute to critical, strategic initiatives and shape the future of analytics, data visualization, and reporting within our Core Products organization.

What You’ll Do

Data Visualization & Report Development

• Own the development, maintenance, and delivery of analytics-ready data platforms, driving data management best practices to deliver clean, standardized, and well-documented data assets using Snowflake, dbt, and Looker.
• Foster strong partnerships with business stakeholders to understand requirements and deliver actionable analytics and reporting that surface key provider sales, operational, and business performance metrics, trends, and insights.
• Partner with the Data team during the design, build, and testing of certified datasets in dbt and Snowflake, including the ingestion, transformation, and modeling of data from internal and external business systems.
• Oversee the design and development of interactive and performant analytical experiences, with primary ownership of Looker dashboards, Explores, and semantic models supporting provider sales, commercial, and operational analytics.
• Design and maintain analytics models and metrics supporting provider sales performance, including pipeline, revenue, customer engagement, operational KPIs, and other key business performance metrics.
• Formulate and implement techniques for auditing and evaluating data collection quality, ensuring adequacy, accuracy, and legitimacy of data.
• Identify, design, and implement internal process improvements, such as optimizing data delivery and redesigning tools and data models for maximum impact.
• Mentor and support the growth of junior team members through coaching, knowledge sharing, and regular feedback.
• Champion and uphold data governance practices by promoting data quality, documentation standards, and consistent metric definitions across data models, semantic layers, and reporting tools.

Stakeholder Engagement

• Be infinitely curious about data and build actionable insights through a deep understanding of the business and the data we collect.
• Drive a culture of storytelling through data by enabling consistent, trusted, and well-modeled datasets that empower self-service analytics and informed decision-making.
• Lead cross-functional collaboration with business leaders, analysts, data scientists, and engineers to understand analytical needs and architect data models and analytics pipelines that support provider sales, commercial operations.