Manager Data Engineering

Ascend Learning, LLC•Gilbert, OR

6d•Hybrid

About The Position

Ascend Learning, a leading healthcare and learning technology company, is the connection between a powerful portfolio of brands serving students, educators, and employers with outcomes-based, data-driven solutions across the lifecycle of learning. From testing to certification, Ascend Learning products are used by physicians, emergency medical professionals, nurses, allied health professionals, certified personal trainers, financial advisors, skilled trades professionals and insurance brokers. Headquartered in Burlington, MA, with additional office locations and hybrid and remote workers in cities across the U.S., Ascend Learning was recognized by Newsweek and Plant-A Insights Group as one of America’s 2025 Greatest Workplaces as well as America’s Best Places to work for Mental Well-Being for 2025. We're always looking for talented, passionate professionals to join us in our mission to help change lives. If this sounds like an environment where you'd thrive, read on to learn more. For over 37 years, National Academy of Sports Medicine® (NASM®) has set the standard in fitness and wellness credentials and certification with best-in-class, evidence-based programming. An Ascend Learning brand, NASM has delivered over 1.4 million fitness professionals in over 100 countries. From personal training to nutrition coaching, wellness to sports performance, NASM trainers and specialists work at every level to help clients set and reach their unique goals. Join us in igniting potential and making a lasting impact in the fitness and wellness industry. We are seeking a strategic and hands-on Senior Manager of IT – Data Warehousing & Analytics to lead the design, development, and operation of our enterprise data platform. This role will be responsible for overseeing the implementation of a modern data warehouse using Snowflake and medallion architecture principles, with Tableau as the primary front-end reporting tool. The position is critical in establishing a scalable, governed data foundation to support business intelligence today—and enable advanced analytics and AI initiatives in the future.

Requirements

Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field.
8+ years of experience in data engineering, data warehousing, or analytics, including 3+ years in a leadership or managerial role.
Proven experience with Snowflake and cloud-based data warehouse architecture (AWS, Azure, or GCP).
Deep understanding of medallion architecture and its application in scalable data solutions.
Strong experience with Tableau or similar BI tools (Power BI, Looker).
Knowledge of ETL/ELT processes, data pipelines (e.g., dbt, Airflow, Fivetran), and modern data engineering best practices.
Experience establishing data governance, lineage, and cataloging frameworks (e.g., Alation, Collibra, Unity Catalog).
Familiarity with AI/ML platforms and the data requirements for operationalizing advanced analytics.
Strong leadership, project management, and communication skills.
Experience in highly regulated or enterprise-scale environments.
Knowledge of data privacy, compliance, and ethical considerations in data & AI.

Nice To Haves

Certification in Snowflake, Tableau, or cloud data platforms is a plus.

Responsibilities

Lead the architecture, design, and development of the enterprise data warehouse platform on Snowflake using medallion architecture (Bronze/Silver/Gold layers).
Oversee data ingestion, transformation, and modeling pipelines to ensure data accuracy, scalability, and performance across the business.
Manage a team of data engineers, BI developers, and analysts responsible for data integration, quality, governance, and reporting.
Build and optimize Tableau dashboards and reporting solutions to deliver actionable insights to business stakeholders.
Define and enforce data governance, security, and quality standards across data platforms.
Collaborate with business units, product owners, and executive leadership to define key metrics and support data-driven decision-making.
Lay the technical and architectural groundwork for AI/ML readiness, including data cataloging, feature engineering pipelines, and integration points with AI platforms.
Drive the roadmap for the data platform, balancing short-term delivery needs with long-term innovation and scalability.
Manage vendor relationships, contracts, and technical partnerships to support the data stack.