$249,000 – $348,500
Listed on Expedia Group’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This is a strategic data engineering leadership role at Expedia's CTO Office, defining how the company transforms operational data into AI-powered executive insights. It suits experienced data engineers comfortable with LLMs and agentic AI systems who want to shape reporting standards across a large travel technology organization.
Our summary, not Expedia Group’s wording. The full posting is on their site.
Skills this role names
- Apache Flink
- Apache Iceberg
- Databricks
- Google Vertex AI
- LangChain
- LlamaIndex
- Looker
- Prompt Engineering
- Python
- Retrieval-Augmented Generation (RAG)
- Snowflake
- SPARK
- SQL
- Tableau
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What they ask for
Required
- Bachelor's degree in Computer Science, Information Systems, Data Science, Business Intelligence, or related field, or equivalent professional experience
- 10+ years in data, analytics, business intelligence, reporting, or related roles (or 12+ years with a Bachelor's degree)
- Proficiency in Python and SQL for data engineering and pipeline development
- 3+ years building or deploying AI/ML or LLM applications including prompt engineering, RAG architectures, or agentic workflows
- 2+ years with an enterprise AI platform such as Azure OpenAI, AWS Bedrock, or Google Vertex AI
Nice to have
- 5+ years leading cross-functional data or analytics programs at Principal level or equivalent scope
- Experience designing and operating agentic AI systems for reports, data questions, or metrics monitoring
- Familiarity with natural language interfaces for analytics including NL-to-SQL and conversational BI
- Understanding of AI output governance including evaluation frameworks and confidence scoring
- Experience with modern data platforms such as Spark, Flink on Iceberg, Snowflake, Databricks, or Delta Lake
- Knowledge of real-time or event-driven data patterns
- Strong executive communication skills
- Working knowledge of responsible AI principles including explainability and auditability