Director, Data Engineering

Join The Best Team In Sport & Spirit

At Varsity Brands, we believe every student deserves the opportunity to succeed and every educator wants to make a difference. It takes a team to make a real impact, and through our two divisions – BSN SPORTS and Varsity Spirit – and our network of 6,000+ employees and independent representatives, we are proud to partner with a wide range of educational institutions and club and professional sports to transform the student journey in SPORT and SPIRIT.

Location: Remote

What You Will Do:

The Director, Data Engineering will lead the team responsible for designing, building, operating, and continuously enhancing Varsity Brands' enterprise data platform. This role will own the strategy and execution for our Snowflake-based data warehouse and future lakehouse capabilities, ensuring the platform delivers trusted, scalable, and secure data products that support analytics, business intelligence, operational reporting, and AI use cases.

Reporting to the SVP, Data and AI, this leader will oversee data engineering, database engineering, replication, transformation pipelines, data governance, and foundational data services. The Director, Data Engineering will partner closely with the leadership, enterprise architecture, application engineering, security, infrastructure, product, and business teams to modernize data capabilities and establish engineering practices that enable speed, reliability, governance, and measurable business value.

How You Will Do It:

Data Platform Leadership

• Lead the enterprise data engineering team responsible for the Snowflake-based data warehouse and future lakehouse architecture
• Define and execute the roadmap for scalable, secure, and high-performance data platform capabilities
• Establish platform patterns that enable analytics, reporting, data science, machine learning, and generative AI use cases
• Partner with the leadership to align platform investments with business priorities, enterprise architecture, and AI strategy
• Drive modernization of data architecture, including data modeling, ingestion, storage, transformation, orchestration, metadata, observability, and governance

Data Engineering & Pipelines

• Own the design, build, and operational support of data replication and transformation pipelines across enterprise systems
• Ensure pipelines are reliable, testable, observable, and optimized for performance, cost, and data quality
• Lead the implementation of engineering standards for ELT/ETL, orchestration, CI/CD, monitoring, incident response, and production support
• Build reusable frameworks and patterns that improve delivery speed and reduce operational complexity
• Ensure data products are accurate, well-documented, discoverable, and aligned to business definitions

Database Engineering

• Lead database engineering for OLTP databases, Operational Data Stores, and Master Data Management platforms hosted in AWS
• Oversee database design, performance tuning, reliability, scalability, backup/recovery, monitoring, and lifecycle management
• Partner with application engineering teams to support transactional database needs and operational data patterns
• Establish standards for database security, access management, schema changes, release practices, and operational excellence
• Support enterprise MDM capabilities that improve consistency, quality, and trust in core business data

AI & Advanced Analytics Enablement

• Ensure the data platform provides foundational data assets required to support AI, machine learning, and advanced analytics
• Collaborate with data science, AI engineering, analytics, and business stakeholders to identify, prioritize, and deliver high-value data products
• Enable governed access to curated, high-quality data for experimentation, model training, inference, reporting, and decision support
• Support the evolution of data architecture to meet emerging AI and lakehouse requirements

Data Governance Program Leadership

• Lead the implementation and ongoing oversight of the enterprise data governance program
• Establish governance standards, operating models, stewardship practices, data ownership frameworks, and decision-making processes
• Partner with business and technology leaders to define critical data domains, data owners, data stewards, and governance priorities
• Oversee data quality, metadata management, lineage, cataloging, access controls, retention practices, and policy adherence
• Ensure governance practices are embedded into data platform delivery, data pipeline development, database engineering, analytics enablement, and AI use-case support
• Drive alignment between data governance, security, privacy, compliance, enterprise architecture, and business reporting needs
• Promote a culture of trusted, well-documented, responsibly managed data across the organization

Leadership Responsibilities

The Director, Data Engineering will lead and develop a high-performing data engineering organization while establishing the operating model, talent, and engineering practices necessary to deliver secure, scalable, and reliable enterprise data platforms:

• Lead, coach, and develop a team of data engineers and database engineers
• Build a culture of technical excellence, accountability, collaboration, documentation, and continuous improvement
• Define roles, delivery processes, performance expectations, and career development paths for the team
• Manage vendor, platform, and implementation partners as needed
• Translate technical complexity into clear business-facing roadmaps, tradeoffs, risks, and outcomes

Success Metrics

Within the first 12–18 months, success will include:

• Deliver a scalable, secure, and highly available enterprise data platform supporting analytics, reporting, and AI initiatives
• Improve data pipeline reliability, observability, and data quality across enterprise systems
• Establish enterprise data governance standards, stewardship, and operating model adoption
• Develop and retain a high-performing data engineering organization with clearly defined roles and career paths
• Deliver prioritized platform modernization initiatives on schedule and aligned with business priorities
• Improve platform performance, operational efficiency, and cloud cost optimization
• Increase business adoption of trusted enterprise data products and self-service analytics

Qualifications:

Knowledge, Skills, Abilities

The ideal candidate will demonstrate the following leadership capabilities:

• Strategic platform thinking
• Enterprise data architecture
• Engineering excellence
• Operational reliability
• People leadership and talent development
• Stakeholder partnership
• Data governance mindset
• Business outcome orientation
• Change leadership
• Clear executive communication

Education, Certification and Experience

Required

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Management, or a related field; equivalent experience will be considered
• 10+ years of experience in data engineering, database engineering, data platform architecture, or related technology roles
• Strong hands-on background with cloud data platforms
• Strong hands-on experience with AWS-hosted database and data services
• Strong understanding of OLTP database platforms, Operational Data Stores, and Master Data Management concepts
• Experience building and supporting enterprise-grade data replication, ingestion, and transformation pipelines
• Familiarity with modern data engineering practices including orchestration, CI/CD, automated testing, data quality, observability, and production support
• Strong understanding of data modeling, SQL performance optimization, metadata management, access controls, and data governance
• Proven ability to partner with business, analytics, AI, application engineering, security, and infrastructure teams
• Strong communication skills with the ability to influence technical and non-technical stakeholders

Preferred

• Experience with lakehouse architecture and open table formats
• Experience supporting AI, machine learning, or advanced analytics platforms
• Experience with data transformation tools such as dbt or similar frameworks
• Experience with replication, CDC, or streaming technologies
• Experience with data catalog, lineage, quality, and observability tools
• Experience leading data platform modernization or cloud migration initiatives
• Experience in retail, sports, education, events, manufacturing, distribution, or multi-brand enterprise environments

Physical Demands and/or Work Environment