Job Description
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.
Sabre is the global leader in innovative technology that powers the travel industry. We are always looking for bright and driven technologists who thrive on solving complex problems at scale. If you are passionate about building highly scalable data pipelines, working with global teams, and applying modern DataOps practices, then Sabre is the right place for you.
The Revenue Optimization Team is expanding as we modernize our data platforms and move seamlessly to the cloud to build the next generation of data and analytics solutions. We are seeking a Software Engineer with hands on expertise in distributed data processing, orchestration, and data architecture on Google Cloud Platform (GCP).
Responsibilities
Data Engineering & Pipeline Architecture
Partner with Product Management to translate business requirements into scalable, cloud-native data solutions.
Design, develop, and deliver high-performance batch and streaming data pipelines using Apache Spark and Apache Airflow.
Contribute to the modernization of data tech stacks, adopting cloud-native patterns and managed GCP services (e.g., Cloud Composer, Dataproc).
Analyze and improve the efficiency, scalability, reliability, and cost of cloud-hosted data workloads.
Architecture & Platform Leadership
Collaborate with Architects to define and evolve the technical data roadmap.
Follow the best practices for data architecture, including stateless processing, asynchronous ingestion, and event-driven data streaming.
Adopt new technologies that improve data processing performance, reliability, and developer productivity.
DataOps, CI/CD & Operations
Design, implement, and maintain CI/CD pipelines for data engineering workflows on GCP.
Contribute to automation across build, test, deployment, and infrastructure provisioning.
Work closely with platform and SRE teams to ensure strong observability, monitoring, and alerting for data pipelines.
Troubleshoot and resolve complex production issues in distributed data systems.
Contribute to cost-aware engineering, balancing performance, reliability, and cloud spend.
Required Skills
Data Engineering Core
Must have:
2+ years of experience building enterprise-grade software or data applications.
Hands-on experience in distributed data processing using Apache Spark.
Hands-on experience orchestrating complex DAGs using Apache Airflow.
Strong proficiency in Python for data manipulation, scripting, and pipeline development.
Good to have:
Data Modeling & Architecture: Experience with relational and dimensional data modeling techniques (e.g., Star Schema, Data Vault) for data warehouses.
Modern Data Stack Tools: Knowledge in data transformation and modeling tools like dbt (data build tool), and an understanding of Lakehouse architectures (e.g., Apache Iceberg, Delta Lake, or Apache Hudi).
Big Data File Formats: Familiarity with optimized, columnar storage formats such as Parquet, Avro, or ORC.
Data Governance & Quality: Experience implementing automated data quality testing, tracking data lineage, and ensuring data security and compliance.
Bonus: Experience building cloud-native microservices and RESTful APIs with Java and Spring Boot is a plus.
Google Cloud Platform (GCP)
Experience with GCP services, such as: BigQuery, Cloud SQL, Firestore, Pub/Sub, Google Kubernetes Engine (GKE), Cloud Run, and Cloud Monitoring & Logging.
Understanding of cloud-native networking, security, and IAM concepts.
Data Platform & DevOps
Knowledge in containers and orchestration (Docker, Kubernetes).
Familiarity with CI/CD pipelines, Git-based workflows, and DataOps automation.
Familiarity with Infrastructure as Code (Terraform or equivalent).
Familiarity with monitoring, logging, and alerting for distributed data systems.
Engineering Excellence
Strong problem-solving, analytical, and communication skills.
Willingness to learn new technologies and innovate when standard solutions are insufficient.
Qualifications
Bachelor’s Degree in Computer Science, Data Engineering, or related field (Master’s Degree preferred).
2+ years of professional experience building and operating enterprise-grade systems.
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.