Senior Data Engineering Manager

Senior Data Engineering Manager

We are a fast-growing vertical SaaS company leveraging innovation and disruptive technologies to improve consumer experiences, outcomes, and predictability within the plastic surgery industry. Our team thrives on challenges, embraces change, and is dedicated to transforming how the industry operates. Data is a critical product within our company, and we are investing heavily in our data capabilities. In this role, you will lead one of the most impactful teams in the organization, with significant opportunity for growth and influence.

We are seeking an experienced Senior Data Engineering Manager to lead and scale our data engineering organization. You'll build and optimize our modern data platform, lead a distributed engineering team, and partner with product, analytics, and business leaders to ensure data is accurate, accessible, and scalable. This role offers full autonomy, leadership opportunities, and direct impact on our data architecture and product capabilities. In this role, you will strengthen our analytics capabilities, partner closely with business leaders, and ensure the delivery of high-impact, actionable insights that drive decision-making across the organization.

While we are a remote-first company, we are currently only able to hire candidates located in the following U.S. states: CA, CO, FL, GA, IL, MN, OK, OR, PA, RI, TX, UT, and WA. We hope to expand to additional states in the future.

As the Senior Data Engineering Manager, you will:

Team Leadership & Strategy

• Lead, mentor, and grow a distributed analytics team across multiple time zones.
• Establish priorities, set goals, and manage performance to foster a high-performing analytics organization.
• Develop and implement analytics standards, best practices, and processes to ensure quality and consistency.

Platform & Pipeline Engineering

• Architect, build, and maintain scalable ETL/ELT pipelines, event streams, and ingestion frameworks to support analytics, product features, and ML applications.
• Oversee the design and implementation of cloud-native data infrastructure (data warehouses, lakes, transformation layers, and orchestration).
• Ensure high data quality, consistency, governance, and observability across all data systems.
• Oversee and guide the development of dashboards, automated reports, and analytics tools using BI platforms (e.g., Tableau, Looker, Power BI, Fivetran, or Redshift).
• Ensure robust data pipelines, reliable metrics, and scalable reporting solutions in collaboration with data engineering.

Project Delivery

• Prioritize analytics projects, balance roadmap demands, and ensure timely delivery.
• Champion analytical excellence, including experiment design, KPI definition, and model validation.