VP, Data Engineering

About the role

The Vice President of Data Engineering will lead AXS’s global data engineering organization, owning strategy, architecture and operation of the data lake, warehouse, batch and streaming pipelines, and governance practices. Reporting to senior leadership, the VP will define the platform roadmap, ensure scalability, security and cost‑effectiveness, and build a high‑performing worldwide team while partnering with product, data science, finance and other business units.

About the company

AXS, a subsidiary of AEG, is a global leader in live event ticketing and e‑commerce, processing over $4 billion in annual transactions and selling millions of tickets for 1,600+ venues worldwide. Founded in 2011, the Los Angeles‑based company employs 900+ professionals across multiple continents and focuses on transforming live entertainment through technology.

Requirements

• 13+ years in high‑growth technology, with 7+ years leading data engineering or data platform teams at scale.
• Experience building and operating high‑volume data platforms for consumer‑facing, e‑commerce or transactional systems.
• Proven success defining and delivering data platform roadmaps, managing multiple initiatives end‑to‑end, and scaling data engineering organizations.
• Strong background with modern data stacks: data warehousing (Snowflake, Redshift, or BigQuery), orchestration (Airflow, dbt), streaming/event‑driven systems (Kafka, Kinesis), and scalable cloud platforms (preferably AWS).
• Expertise in data pipeline architecture, data quality frameworks, observability, CI/CD, and SRE practices for high reliability and data trust.
• Skilled with enterprise and NoSQL databases (Oracle, DynamoDB, Snowflake, etc.) and designing systems for personalization, analytics, and real‑time decisioning.
• Experience implementing data governance, lineage, and security practices, including compliance with data privacy and payment card standards.
• Deep knowledge of Scrum/Kanban and ability to optimize processes, define KPIs, and improve delivery at scale.
• Success leading distributed, diverse global teams and building collaborative, innovative high‑performance cultures.
• Strong ability to partner with Data Science, Product, Engineering, and Business Operations to align data infrastructure with company priorities.
• Experience managing third‑party vendors and outsourcing partnerships, ensuring quality and strategic alignment.