Manager, Data Engineering

Overview

Crocs is seeking a Manager, Data Engineering to lead the evolution of our enterprise data platform and guide a team of skilled engineers in building scalable, reliable, and future-ready data infrastructure. This role is responsible for setting the technical vision, shaping best practices, and ensuring smooth delivery of data solutions that power analytics, reporting, and advanced AI initiatives across the business.

You’ll be a key contributor to both strategy and execution—balancing hands-on technical oversight with people leadership. The ideal candidate combines deep technical expertise in modern data engineering tools (Snowflake, PySpark, Airflow, Azure) with a strong track record of building and mentoring high-performing teams. As a bridge between engineering and business, you’ll help ensure Crocs’ data ecosystem robust, efficient, and aligned with long-term enterprise goals.
 

What You'll Do

• Team Leadership – Manage, mentor, and grow a high-performing team of data engineers. Provide technical direction, career development support, and day-to-day coaching.
• Technical Ownership – Oversee design, implementation, and maintenance of scalable data pipelines, data models, and orchestration frameworks.
• Architecture & Standards – Drive adoption of modern data engineering practices, including modular data modeling, CI/CD pipelines, test automation, and performance optimization.
• Project Delivery – Ensure timely and reliable delivery of data engineering projects while balancing long-term platform stability and scalability.
• Operational Excellence – Proactively identify and resolve issues related to data integrity, data quality, and system performance. Champion automation and monitoring practices.
• Documentation & Governance – Promote strong documentation standards and contribute to data governance and compliance initiatives.
• Cross-Functional Collaboration – Work closely with analytics, data science, business intelligence, and IT teams to align technical solutions with business needs.
• Continuous Improvement – Encourage a culture of learning by staying current with advancements in the data ecosystem and exploring emerging technologies in AI/ML, streaming, and real-time data processing.
• Data Modeling – Design, implement, and maintain scalable data models that support the Enterprise Data Warehouse (EDW) and analytical workloads following best practices and restrictions imposed by respective technologies.
• ETL/ELT – design and implement efficient, scalable and easy-to-manage data movement processes supporting both batch and near-real-time data streams.
• CI/CD – automate code integration, testing, and deployment using Git to ensure fast, reliable, and consistent delivery of data pipelines and ETL code.
   

What You'll Bring to the Table

• Bachelor’s degree or equivalent experience in computer science, information technology, engineering, mathematics, or equivalent technical degree.
• 8+ years in Data Engineering roles, with 2+ years in management capacity.
• 4+ years of direct development in Snowflake, Snowflake certifications preferred.
• Strong proficiency in Python, PySpark, and Apache Airflow.
• Experience utilizing Git version control in a data environment, working with/in GitHub, GitActions.
• Experience designing data models following Kimbal Dimensional Modeling best practices.
• Experience working with modern ETL/ELT tools, such as Databricks.
• Prior experience working in a cloud platform (Azure preferred).
• Experience with a business intelligence tool, ideally Power BI, preferred.
• Experience working with Azure Data Factory, preferred.