Manager, Analytics Engineering
- Compensation
- Not disclosed
- Location
- Cincinnati, Ohio, United States; New York City, NY; Remote, US
- Work setup
- Remote
- Seniority
- Intern
- Employment
- Internship
- Experience
- 2+ years
- Category
- Not specified
- Posted
Skills
Nice to have
- Experience implementing dbt best practices (modular modeling, testing, documentation, CI/CD).
- Looker and Look ML Expertise
- Deep experience with semantic modeling and BI governance (LookML, Tableau, or equivalent).
- Proven success driving adoption of self-service BI while maintaining governance and trust in data.
- Proficiency in analytics tools such as Python (preferred), alongside SQL, Snowflake, and Looker.
- Strong understanding of data modeling, statistical analysis and data visualization.
- Experience with cloud computing environments (e.g., Snowflake, GCP, AWS, Azure).
- Expertise with relational and cloud-based database systems.
Full 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); New York, NY (Hybrid) ; or remote within the Eastern Time Zone.
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.