Manager, Analytics Engineering

PatientPoint•Cincinnati, OH

7h•$129,700 - $185,400•Hybrid

About The Position

The Manager of Analytics Engineering leads a team responsible for transforming raw data into governed, reusable, and scalable analytics assets. The role oversees dbt models, semantic layers such as LookML, and business intelligence 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 teams to align data products with business priorities. The manager develops analytics engineering strategy and standards, ensuring data assets are accurate, consistent, reliable, well documented, tested, and compliant with company governance standards and applicable regulations. The role shares ownership of the analytics engineering ecosystem, including dbt, Snowflake, and Looker; standardizes the metric layer across BI tools and reports; and enables teams to use trusted data independently. The manager also translates business requirements into technical specifications, communicates insights and recommendations to decision-makers, and establishes and tracks OKRs for strategic initiatives. The position is based in Cincinnati, Ohio, or New York, New York, in a hybrid arrangement, or may be remote within the Eastern Time Zone. Travel is required three to five times per year. The listed base salary range is $129,700 to $185,400. PatientPoint describes its full-time benefits as including competitive compensation, flexible time off, hybrid work options, mental and emotional wellness resources, and a 401(k) plan.

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 convey technical concepts to non-technical audiences.
Experience with project management practices and methodologies.
Strong understanding of data modeling principles and 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.
Exceptional communication and presentation skills, with the ability to influence stakeholders at all levels.
Strong 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 adoption of self-service BI while maintaining governance and trust in data.
Proficiency in analytics tools such as Python, alongside SQL, Snowflake, and Looker; Python is preferred.
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, mentor, and guide a team of data visualization engineers and other professionals.
Foster a culture of continuous learning, innovation, collaboration, accountability, improvement, and growth.
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 to ensure pipelines, tools, and dashboards meet business reporting and analytics needs.
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 a governed set of core metrics.
Enable self-service access to reliable data while avoiding bottlenecks.
Ensure data quality, integrity, and compliance with industry standards, regulations, and company Data Governance standards.
Standardize the company metric layer to ensure consistent definitions across BI tools and reports.
Partner with business stakeholders and data teams to define requirements, set expectations, and communicate effectively.
Translate business requirements 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 strategic initiatives.
Deliver analytics assets that reduce redundancy, accelerate time-to-insight, and promote consistent organizational metrics.