Data Engineer

About Ubeya

Ubeya is a leading B2B SaaS platform built for the workforce industry, making it easy to manage complex operations at scale. From major stadiums to high-profile sports and entertainment events, Ubeya powers staffing for Football, Basketball, Baseball, Racing, and more — helping top venues deliver seamless, unforgettable experiences.

Our workforce management platform enables hundreds of businesses and staffing agencies worldwide to stay resilient and adaptable in a fast-changing world — driving growth, operational excellence, and a happier, more engaged workforce.

The Role

We are looking for an experienced Data Engineer to join our growing team. As part of our R&D efforts, you will play a pivotal role in designing and building scalable data pipelines and infrastructure, ensuring reliability and efficiency across the full data lifecycle. Your work will have a substantial impact on our product experience and the business.

What You will do:

• Design and build scalable data architectures and pipelines, ensuring reliability and efficiency across the full data lifecycle.
• Optimize and manage data infrastructure, enabling analytics, machine learning, and real-time applications.
• Develop orchestration and monitoring frameworks to support complex workflows.
• Prepare, transform, and serve data for AI/ML models, ensuring quality, accessibility, and performance.
• Collaborate with engineers, analysts, and data scientists to support data-driven decision making.
• Implement and enforce best practices for data governance, quality, and security.
  
  

• 5+ years of proven experience as a Data Engineer or in a similar role, with at least 2+ years delivering production systems.
• Strong proficiency in Python for data processing and automation.
• Deep knowledge of SQL and data modeling.
• Experience with workflow orchestration tools such as Airflow or Dagster.
• Experience with cloud platforms such as GCP, AWS, or Azure.
• Familiarity with modern data warehouse solutions such as Snowflake, BigQuery, or Redshift.
• Experience developing and optimizing ETL/ELT pipelines and transformations.
  
  

• Experience with real-time data streaming technologies (e.g., Kafka, Flink, Spark Streaming).
• Exposure to NoSQL databases (e.g., MongoDB, Elastic, Redis).
• Familiarity with MLOps and deploying machine learning models in production.
• Knowledge of CI/CD and infrastructure-as-code.
• Experience with Node.js or working closely with backend engineering teams.
• Familiarity with AI orchestration tools such as LangChain.
• Prior experience in fast-paced startup environments.