Lead Data & AI Platform Engineer (Remote, United Kingdom)

Job Summary:

JOB DESCRIPTION –Lead Data & AI Platform Engineer
Location: Remote, United Kingdom
Division: Ticketmaster
Contract Terms: Permanent
THE TEAM

The engineering team builds and operates reliable, scalable software products that support high-volume customers, partners, and internal workflows. The team works closely with product, design, data, security, operations, and other engineering groups to deliver resilient systems, improve platform quality, and create better experiences for users.
THE JOB

Ticketmaster is building a digital-first customer operations ecosystem powered by data, automation, and AI. The Lead Data & AI Platform Engineer designs and scales the platforms that support customer service, workforce optimization, automation, and conversational AI across global markets.

This is a senior, hands-on leadership role. You will set platform and engineering standards, coach and develop engineers and data scientists, and may directly manage individuals as the team grows. You will also design and build Databricks Genie experiences that allow business users to safely ask questions of trusted data and receive useful, explainable answers.

The role partners with Product, Operations, Engineering, Workforce Management, and AI teams to deliver reliable, scalable solutions that improve customer outcomes, operational performance, and AI effectiveness.
WHAT YOU WILL BE DOING

- Lead the architecture and ongoing evolution of the Databricks platform for customer operations, analytics, automation, and AI use cases across global markets.

- Design, build, review, and optimize scalable Databricks pipelines that ingest data from enterprise and customer-support systems into trusted, reusable data products.

- Establish clear data architecture, modeling, quality, governance, lineage, access, observability, and reliability standards using Databricks and Unity Catalog.

- Design, build, and continuously improve Databricks Genie spaces, including curated data domains, semantic models, business definitions, trusted metrics, verified queries, permissions, and answer evaluation.

- Use Databricks AI capabilities to analyze operational data, surface trends and opportunities, and make trusted intelligence available through Genie, dashboards, APIs, and approved workflows.

- Apply machine learning, NLP, Generative AI, forecasting, and operational analytics where they provide clear business value, with appropriate evaluation and lifecycle controls.

- Partner with Product, Operations, Workforce Management, Engineering, and AI teams to turn business questions into scalable data and intelligence solutions.

- Provide hands-on technical leadership through design reviews, coding, troubleshooting, platform optimization, and engineering standards.

- Coach and mentor engineers and data scientists, helping them grow their Databricks, data engineering, analytics, and AI capabilities.

- Manage individual contributors where required, including goal setting, development, feedback, performance support, and creating space for innovation and career growth

WHAT YOU NEED TO KNOW (or TECHNICAL SKILLS)

- Strong hands-on experience with Databricks, Spark/PySpark, Delta Lake, cloud data platforms, and enterprise-scale data pipelines.

- Hands-on experience designing and operating Databricks lakehouse architectures, including governed data products, semantic layers, trusted metrics, data quality, and role-based access.

- Hands-on experience building and operating Databricks Genie or closely comparable natural-language analytics experiences. You should understand how to prepare data and semantic models so users can ask reliable business questions and receive grounded answers.

- Experience with Databricks governance and platform capabilities such as Unity Catalog, lineage, access controls, CI/CD, monitoring, and observability.

- Experience building batch and streaming ingestion pipelines and integrating structured and unstructured data from multiple