VP Data Engineering

Role Overview The VP, Data Engineering and Science will be responsible for leading the design, implementation, and continuous improvement of the organizations data infrastructure and architecture. This role requires a blend of technical leadership and operational strategy, with a primary focus on overseeing the overall architecture that supports the companys data needs. Additionally, this position will manage and collaborate with outsourced development teams, ensuring that they align with the business's security and design policies.
The individual in this role will ensure the scalability, security, and efficiency of the data ecosystem while integrating with broader business objectives. Expertise in cloud data platforms, architecture design, and data governance is essential.

The VP, Data Engineering and Science role is positioned within TKOs Data Services department, which oversees all data-related applications across the companys global business units. This includes a broad range of responsibilities, from owning the data platform, creating data pipelines, data transformations, to machine learning and advanced analytics, ensuring seamless integration and support for the organizations diverse data needs.

Thought Partnership
Partner with leaders across the TKO business units including marketing, sales, product, and analytics to anticipate industry trends, shape the technical data and machine learning vision, and turn business goals into executable forward-thinking strategies and solutions.
Act as a trusted technical advisor to senior leadership, framing buildvsbuy decisions, articulating risks, and recommending tools, processes, and sequencing for maximum impact.
Own the technical roadmap for data architecture, engineering, and science, ensuring alignment of data initiatives with organizational priorities. Proactively identify opportunities for platform evolution and set clear milestones to keep pace with technological and business advancements.

Lead, hire, and develop an organization of data platform engineers, analytics engineers, and data scientists; establish career paths, mentorship, and feedback mechanisms that foster learning, inclusion, and innovation.
Oversee outsourced teams to ensure effective communication, project delivery, and alignment with business goals, while upholding company design standards and ensuring high-quality outcomes.

Engineering & Science Excellence and Platform Ownership
Own a modern, highlyscalable data platform (cloud, warehouse, orchestration, observability): design and evolve the enterprise data architecture and integration patterns using best practice and innovation.
Establish and lead robust architectural and design governance frameworks that guide the evolution of the data platform and adjacent systems.
Champion infrastructure-as-code for platform resources, CI/CD, and change management. Promote a strong DevOps culture by integrating automated testing, deployment pipelines, and seamless collaboration between teams.
Foster a security first culture and embed security by design principles into every layer of the data platform architecture, ensuring that data protection, privacy, and compliance are foundational and proactively addressed from inception through deployment. Stay ahead of emerging threats and regulatory requirements.
Establish comprehensive monitoring and logging frameworks to ensure real-time insight into data and analytics infrastructure. Standardize logging practices across platforms to enable rapid incident detection and compliance auditing. Integrate advanced alerting and anomaly detection to proactively address performance and security issues.
Set strategy for ML initiatives, from feature/pipeline reuse and deployment patterns to monitoring for drift and model lifecycle management.
Advance emerging agentic AI/automation initiatives where appropriate to accelerate data engineering and analytics workflows.

8+ years of engineering leadership, comfortable managing multidisciplinary teams (data engineering, platform/DevOps, analytics engineering, data science); Proven success leading enterprise warehouse programs, ideally on Snowflake, including security models, performance/optimization, cost governance, and migration at scale.
~ Expert in data pipeline development and ELT/ETL frameworks; Solid AWS experience (e.g., S3, Lambda, ECS/EKS) and IaC (Terraform, AWS CDK, Helm) with CI/CD and environment management best practices.
~ Experience working closely with data science and analytics teams on feature platforms, deployment patterns, and MLOps; able to translate product/analytics needs into robust data solutions.
~ Experience with data modeling (dimensional/semantic layers), testing/validation, and documentation practices that enable selfserve analytics at scale.
~ We understand this can only be accomplished when we lead with a lens of diversity, equity, and inclusion in everything we do. TKO is an equal opportunities employer and encourages applications from suitably qualified and eligible candidates regardless of sex, race, disability, age, sexual orientation, or religion or belief.

Per local requirements and in the interest of transparency, the range shown below reflects the prevalent current hiring range for this position. The company strives to provide locally competitive rewards packages, which include base rate along with, as applicable, short- and long-term incentives, growth and developmental opportunities, and robust benefits, such as health care, retirement, vacation and other paid time off, and additional offerings.