Sr. Data Science Engineer

Powering the agentic revolution in travel. Sabre is an AI-native technology leader, backed by one of the world s largest travel data clouds. Built on an open, modular, cloud-native architecture, Sabre serves as the backbone for both established leaders and bold, new disruptors, guiding them to the next age of travel retailing through intelligent, connected, and personalized experiences. With AI at its core and operating at unparalleled scale, Sabre transforms insights into innovation, empowering airlines, hoteliers, agencies and other partners to retail, distribute and fulfill travel worldwide.

• Build and operate Apache Airflow DAGs on Cloud Composer, including dependencies, scheduling, and testing.
• Develop batch and streaming pipelines using Apache Beam on Cloud Dataflow.
• Design, optimize, and maintain BigQuery datasets, SQL, partitions, clustering, and cost efficient query patterns.
• Develop and operationalize BigQuery ML (BQML) models within analytics workflows.
• Manage environment aware configuration using YAML across DEV, CERT, and PROD.
• Provision and manage GCP infrastructure using Terraform (GCS, BigQuery, IAM, CMEK).
• Own and extend GitHub Actions pipelines for build, test, and multi environment deployment.
• Apply GitHub Copilot to accelerate development, testing, and code review.
• Lead or support schema evolution and BigQuery DDL migration efforts.

• Python (type hints, clean modules, conventions)
• Apache Airflow
• BigQuery (SQL, DDL, performance, cost optimization)
• BigQuery ML (training, evaluation, inference)
• Google Cloud Platform (GCS, IAM, Composer, Dataflow, CMEK)

• Apache Beam
• Terraform (IaC modules, lifecycle management)
• GitHub Actions (CI/CD, artifacts, multi env deployment)
• YAML based configuration management
• GitHub Copilot / AI assisted development

• Artifact registries (e. g. , Nexus) and deployment orchestration tooling
• CMEK focused security and encryption patterns
• Large scale BigQuery schema evolution and migration experience
• Lakehouse architectures on GCP
• Cross repo or multi team platform engineering experience
• Integrating BQML models into automated Airflow pipelines

• Add a short senior level expectations callout (technical leadership, design ownership)

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