Director, Technology

Expedia Group•Austin, TX

2d•$231,000 - $323,500

About The Position

The Director, Data Engineering leads the Data Engineering, Software Engineering, AI, and Innovation capabilities supporting Expedia Group’s Marketing Automation and MarTech organization. This leader sets the vision and execution strategy for platforms, products, and services that connect customer, marketing, product, and operational data and enable responsible, scalable AI use. Reporting to the head of Digital Operations, the Director ensures data, software, and AI capabilities are trusted, secure, scalable, and easy to use. The role partners across Marketing, MarTech, Analytics, Product, Technology, and responsible AI stakeholders to improve customer engagement, campaign effectiveness, operational efficiency, and the pace of innovation. The successful leader is strategic, outcome-oriented, technically credible, and comfortable working across business and technology. They establish clarity and accountability for reliability, responsible AI, and customer value while fostering an inclusive environment. They balance innovation with risk, cost, scale, trust, and long-term platform health.

Requirements

10+ years of experience in data engineering, software engineering, platform engineering, or a related discipline, including 3+ years leading managers or multidisciplinary engineering teams.
Experience defining and executing technology strategy in complex, enterprise-scale environments.
Track record of building and operating production-grade data platforms, pipelines, integrations, software services, and automation.
Strong knowledge of data architecture, data modeling, ETL/ELT, batch and streaming systems, APIs, event-driven design, orchestration, and distributed processing.
Experience with SQL, Python, cloud platforms such as AWS, GCP, or Azure, and technologies such as Spark, CI/CD, observability, and infrastructure automation.
Experience integrating CRM, marketing automation, customer data, analytics, experimentation, and personalization platforms.
Experience establishing data quality, governance, security, privacy, reliability, responsible AI, and operational excellence practices.
Experience leading software delivery, production operations, incident management, modernization, and platform simplification.
Experience applying machine learning, generative AI, intelligent automation, or AI-enabled engineering practices in production or enterprise environments.
Strong communication and influencing skills, including the ability to explain technical concepts and trade-offs to technical and non-technical audiences.
Track record of building high-performing teams, developing leaders, and creating effective cross-functional partnerships.
Sound judgment balancing innovation and speed with sustainability, control, maintainability, and long-term platform health.

Nice To Haves

Experience with Salesforce Marketing Cloud, Adobe Marketo, Eloqua, or comparable platforms.
Experience with customer data platforms, identity resolution, consent management, audience activation, or personalization ecosystems.
Experience with machine learning, generative AI, predictive analytics, AI agents, model operations, LLM operations, experimentation platforms, or AI-enabled engineering productivity.
Familiarity with responsible AI practices, including evaluation, explainability, safety, human oversight, and lifecycle governance.
Experience designing data products, semantic layers, metrics platforms, or self-service analytics capabilities.
Experience leading cloud migration, platform consolidation, technology simplification, or other multi-year modernization programs.
Familiarity with GDPR, CCPA, or comparable privacy and data protection requirements.
Experience managing strategic technology vendors, external partners, and enterprise platform dependencies.
Experience working in a global, matrixed, consumer-facing, or high-volume digital environment.

Responsibilities

Set and execute a multi-year data and technology strategy aligned to Marketing Automation, MarTech, personalization, customer engagement, and Digital Operations priorities.
Lead data engineering and platform ownership, software engineering and integration services, and AI, innovation, experimentation, and emerging technology capabilities.
Define architecture standards and make build, buy, partner, and platform decisions that balance speed, scale, reliability, security, and cost.
Build and evolve scalable data pipelines, data products, APIs, integrations, services, workflow automation, and developer tooling across the MarTech ecosystem.
Establish practices for data quality, observability, lineage, access, governance, privacy, security, service ownership, and incident response.
Partner with Analytics and business stakeholders to create trusted metrics, semantic models, and self-service capabilities.
Enable advanced analytics, experimentation, machine learning, generative AI, and intelligent automation with fit-for-purpose data and engineering foundations.
Establish AI adoption foundations, including model and prompt evaluation, data readiness, monitoring, cost management, access controls, human oversight, and responsible AI guardrails.
Lead innovation from problem discovery and prototyping through evaluation, productionization, or intentional shutdown, ensuring successful experiments become supported and measurable products.
Align stakeholders on roadmaps, priorities, dependencies, investment choices, risks, and trade-offs, and communicate progress to senior leadership.
Build, lead, and develop a high-performing organization of engineering leaders and individual contributors across data, software, and innovation.
Establish an operating model that balances committed delivery, platform health, technical debt reduction, modernization, and responsible experimentation.
Improve efficiency through automation, reusable platforms, technology simplification, cloud and vendor cost management, and disciplined portfolio planning.