Manager, Analytics Engineering

PatientPoint•Cincinnati, OH

1d•$129,700 - $185,400•Hybrid

About The Position

PatientPoint is seeking a Manager of Analytics Engineering to lead a team that transforms raw data into governed, reusable, and scalable analytics assets. The role oversees dbt models, semantic layers such as LookML, and BI frameworks that enable trusted self-service insights. It combines hands-on analytics engineering expertise with team leadership and close collaboration across Data Engineering, Reporting, Analytics, and Advanced Analytics to align data products with business priorities. The manager will develop analytics engineering strategy and practices, own and evolve the analytics engineering ecosystem—including shared ownership of dbt, Snowflake, and Looker—and establish standards for data modeling, testing, documentation, version control, and metric definitions. The role is also responsible for data quality, governance, and compliance; translating business requirements into technical specifications; communicating insights and recommendations to decision-makers; and delivering reliable analytics assets that improve consistency and time-to-insight. The position is hybrid in Cincinnati, Ohio, or New York, New York, or remote within the Eastern Time Zone. Travel is required three to five times per year.

Requirements

Bachelor’s degree in computer science, Information Systems, Data Engineering, Data Analytics, or a related field.
At least 5 years of experience in a related data visualization or data engineering role.
At least 2 years of experience managing or mentoring a team or contractors.
Advanced experience with dbt and Snowflake.
Advanced SQL knowledge, including complex queries, performance optimization, and work with large datasets.
Proven experience developing and delivering data visualization, reporting, or business intelligence solutions.
Excellent communication skills, including the ability to explain technical concepts to non-technical audiences.
Experience with project management practices and methodologies.
Strong understanding of data modeling principles and the ability to apply statistical and analytical techniques when appropriate.
Ability to lead and inspire teams in a fast-paced environment.
Strong project management skills and a track record of delivering complex projects on time and within scope.
Strong communication and presentation skills, with the ability to influence stakeholders at all levels.
Excellent problem-solving and critical-thinking skills.
Ability to translate analytical concepts into clear, compelling stories for technical and non-technical audiences.

Nice To Haves

Experience implementing dbt best practices, including modular modeling, testing, documentation, and CI/CD.
Looker and LookML expertise.
Deep experience with semantic modeling and BI governance using LookML, Tableau, or equivalent.
Proven success driving self-service BI adoption while maintaining governance and trust in data.
Proficiency in Python, alongside SQL, Snowflake, and Looker.
Strong understanding of data modeling, statistical analysis, and data visualization.
Experience with cloud computing environments such as Snowflake, GCP, AWS, or Azure.
Expertise with relational and cloud-based database systems.

Responsibilities

Lead, manage, train, and mentor a team of data visualization engineers and other professionals.
Foster continuous learning, innovation, collaboration, accountability, and improvement.
Oversee resource allocation, prioritization, and project delivery to achieve high-quality outcomes within deadlines.
Develop frameworks, methodologies, and tools that improve the design and delivery of reliable data assets.
Develop and implement analytics engineering strategies aligned with business objectives, emphasizing repeatable, scalable, and automated solutions.
Ensure the accuracy, consistency, and reliability of data assets.
Collaborate with Data Engineering, Data Reporting, Data Analytics, and Advanced Analytics teams on data pipelines, tools, dashboards, reporting, and analytics.
Own and evolve the 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 governed core metrics.
Enable self-service access to reliable data while maintaining governance and trust.
Ensure data quality, integrity, and compliance with industry standards, regulations, and company data governance standards.
Standardize the company metric layer so definitions are consistent across BI tools and reports.
Partner with business stakeholders and data teams to define requirements, set expectations, and communicate effectively.
Translate business requirements into clear technical specifications.
Present insights and recommendations to executives and other decision-makers.
Establish and track objectives and key results (OKRs) to evaluate strategic initiatives.
Deliver analytics assets that reduce redundancy, accelerate time-to-insight, and promote consistent organizational metrics.