Manager, Analytics Engineering

Job description

Join PatientPoint to be part of a dynamic team creating change in and around the doctor’s office. As a leading digital health company, we **innovate** to positively **impact** patient behaviors. Our purpose-driven approach offers **an inspirational career** opportunity where you can contribute to improving health outcomes for millions of patients nationwide.  **Location:**Cincinnati, OH **Hybrid Schedule** **Travel Requirements:**3 to 5 times per year **Job Summary** The Manager of Analytics Engineering will lead a team responsible for transforming raw data into governed, reusable, and scalable analytics assets. This includes managing dbt models, semantic layers (LookML), and BI frameworks that empower the company with trusted, self-service insights. The role requires hands-on expertise in modern analytics engineering practices, close collaboration with Data Engineering, Reporting, and Analytics teams, and the ability to align data products with business priorities. **What You’ll Do** **Team Leadership:** - Lead, manage, and train a team of data visualization engineers and professionals. - Foster a culture of continuous learning, innovation, collaboration, and improvement. - Oversee resource allocation, prioritization, and project delivery to ensure high-quality outcomes within deadlines. - Provide mentorship and guidance to team members, cultivating a culture of accountability, collaboration, and growth. - Contribute to the development of frameworks, methodologies, and tools that improve how the company designs and delivers reliable data assets. **Data Strategy and Execution:** - Develop and implement analytics engineering strategies aligned with business objectives with a focus on repeatable, scalable, and automated solutions. - Ensure the accuracy, consistency, and reliability of all data assets. - Collaborate with Data Engineering, Data Reporting, Data Analytics, and Advanced Analytics teams to ensure data pipelines, tools, and dashboards for robust reporting and analytics meet the needs of the business. - Own and evolve the companies analytics engineering ecosystem, including shared ownership of dbt, Snowflake, and Looker. - Define and enforce standards for modeling, testing, documentation, and version control in dbt and BI layers. - Partner with stakeholders to establish and maintain a governed set of core metrics. - Champion self-service enablement, ensuring teams can access reliable data without bottlenecks. - Ensure data quality, integrity and compliance with industry standards and regulations and the companies Data Governance standards. - Own the standardization of the companies metric layer to ensure consistent definitions across all BI tools and reports. **Business Impact:** - Partner with business stakeholders and data teams, defining clear requirements, setting expectations, and communicating effectively and efficiently. - Ensure business requirements are appropriately translated into technical specifications that clearly define the work. - Present insights and recommendations to executives and other decision-makers. - Establish and track objectives and key results (OKRs) to evaluate the effectiveness of strategic initiatives. - Deliver analytics assets that reduce redundancy, accelerate time-to-insight, and promote consistency in metrics across the organization. **What We Need** - 5+ years in a related data visualization or data engineering role. - Bachelor’s degree in computer science, Information Systems, Data Engineering, Data Analytics, or a related field. - At least 2 years of experience managing or mentoring a team or contractors. - Proven experience in developing and delivering within data visualization, reporting, or business intelligence. - Advanced experience with dbt, Snowflake, Looker and LookML. - Advanced SQL knowledge, including writing complex queries, optimizing performance, and working with large datasets. - Excellent communication, with the ability to effectively convey technical concepts to non-technical audiences. - Experience with project management practices and methodologies. - Strong understanding of data modeling principles, with the ability to apply statistical and analytical techniques when appropriate. **What You'll Need to Succeed** - Experience implementing dbt best practices (modular modeling, testing, documentation, CI/CD). - Deep experience with semantic modeling and BIgovernance (LookML, Tableau, or equivalent).