Manager of Data Engineering

The Aspen Group (TAG) is one of the largest and most trusted retail healthcare business support organizations in the U.S., supporting 15,000 healthcare professionals and team members at more than 1,000 health and wellness offices across 46 states in four distinct categories:
Dental Care, Urgent Care, Pet Care, and Medical Aesthetics. Working in partnership with independent practice owners and clinicians, the team is united by a single purpose: to prove that healthcare can be better and smarter for everyone. TAG provides a comprehensive suite of centralized business support services that power the impact of four consumer-facing businesses:
Aspen Dental, Clear Choice Dental Implant Centers, Well Now Urgent Care, Lovet Veterinary Clinics and Chapter Aesthetic Studio. Each brand has access to a deep community of experts, tools and resources to grow their practices, and an unwavering commitment to delivering high-quality consumer healthcare experiences at scale.
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* Position Overview:

** As the Manager of Data Engineering at TAG you will be responsible for overseeing a team of data engineers and driving the development and maintenance of our data infrastructure. You will play a critical role in ensuring data quality, transformation, and orchestration while working closely with cross-functional teams to support data-driven decision-making. This position offers a unique chance to lead a team and contribute to the organization's data-driven success.

While you will be in a leadership role you are expected to have hands on keyboard and also contributing to building data pipelines and support Capitalized and Operational Driven Projects.
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* Key Responsibilities:

*** Leadership, Mentorship and Day to Day Management of Data Engineering Pods and Resources
* Development of DBT Pipelines for data transformation, including cataloging, lineage, freshness, and data quality checks. Implement best practices to optimize data transformation processes.
* Code Reviews:
Conduct thorough code reviews to ensure the quality, efficiency, and maintainability of data engineering code. Provide constructive feedback and mentor team members to improve their skills.
* Governance and Best Practices:
Hold the data engineering team accountable for adhering to code quality, dbt best practices, and data governance standards. Establish and enforce coding standards and guidelines.
* Infrastructure Management:
Collaborate with the Data Platform Team to define and maintain the infrastructure for data engineering code. Ensure scalability, reliability, and performance of data pipelines.
* Airflow and DAG Quality:
Implement and maintain Airflow workflows and DAGs (Directed Acyclic Graphs) to orchestrate data pipelines effectively. Ensure the quality and reliability of workflow scheduling and execution.
* Google Cloud Technologies:
Leverage Google Cloud Platform (GCP) technologies such as Pub/Sub, Data Flow, Cloud Functions, Big Query, and Cloud Storage to design and optimize data solutions.
* Team Leadership:
Lead and mentor a team of data engineers, fostering a culture of collaboration, continuous learning, and innovation. Provide guidance on technical and career development.
* Data

Collaboration:

Collaborate closely with data scientists, analysts, and other stakeholders to understand their data requirements and provide data engineering support.
* Agentic AI and RAG Integrations:
Stay updated with the latest advancements in Agentic AI and RAG Technologies. Evaluate and integrate as and when appropriate into data engineering processes to enhance data retrieval, generation and overall efficiency
* Lead the integration of Vibe coding practices into data engineering processes. Define and enforce guardrails to ensure ethical and responsible usage of Vibe Coding, collaborate with cross functional teams to establish best practices and help mentor the team the important of better prompting, style guides and interfacing with tooling choices to get more accurate, smarter and better results
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* Qualifications/Requirements:

*** Bachelor's degree in a relevant field (e.g., Data Science, Computer Science, Business Analytics); master’s degree preferred.
* Proven…