Lead Data Science Engineer

Responsibilities

• 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.
• Required Skills
• Strong:
• 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)
• Proficient:
• Apache Beam
• Terraform (IaC modules, lifecycle management)
• GitHub Actions (CI/CD, artifacts, multi‑env deployment)
• YAML‑based configuration management
• GitHub Copilot / AI‑assisted development

Requirements

• 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
• Senior‑level expectations callout (technical leadership, design ownership)
• We will give careful consideration to your application and review your details against the position criteria. You will receive separate notification as your application progresses.
• Please note that only candidates who meet the minimum criteria for the role will proceed in the selection process.
• #LI-Hybrid#LI-GR1

Benefits

Additional Information

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. Cloud‑Native Analytics Pipelines (GCP) Service Time: Typically 7+ years of progressive experience in data engineering, analytics engineering, or applied data science roles Role Overview We are seeking a Senior Data Science Engineer to design, build, and operate production‑grade analytics pipelines on Google Cloud Platform. This role spans the full lifecycle-from orchestration and transformation to analytics, ML, and infrastructure-using modern CI/CD and infrastructure‑as‑code practices.

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