Data Engineer

About the role

Join Atlas Air Worldwide. Move the World with Us!

At Atlas Air Worldwide, we’re not just an airline, we’re a global engine powering cargo, passenger, and leasing operations across more than 70 countries. As a leader in outsourced aviation logistics, we’re built on a foundation of safety, service excellence, integrity, innovation, teamwork, and responsibility. With over 30 years of history, a modern all-Boeing fleet, and nearly 5,000 teammates collaborating across operations, technical, and corporate functions, we’re driven by purpose.

If you're ready to grow, innovate, and help us deliver excellence every single day, you belong here.

Position Summary: Atlas Air is seeking a Data Engineer to join our Data Platform and Engineering team. This is an early-career individual contributor role for an engineer who can deliver well-defined data solutions with guidance, build strong engineering fundamentals, and grow ownership over increasingly complex work.

As we continue to build our data environment on Databricks, you will help build and operate reliable data pipelines, transformations, and data models. You will work within established architecture and standards, partner with more experienced engineers, and author production-quality code that supports analytics, data science, AI/ML, and operational decision-making.

Major Accountabilities:

Data Pipeline Development

- Build, test, deploy, and maintain batch and streaming data pipelines for structured and semi-structured data sources using established patterns and frameworks.

- Develop data transformations that convert source data into accurate, documented, and reusable datasets.

- Implement data models and data products based on defined business requirements and technical designs.

- Use Databricks, Spark, orchestration tools, and cloud services to complete scoped engineering work with support from senior team members.

- Participate in code reviews and incorporate feedback to improve code quality, maintainability, and performance.

Data Quality & Operations

- Create automated tests, validation checks, logging, and monitoring the pipelines and datasets you develop.

- Investigate data quality, reliability, and performance issues; resolve routine issues independently and escalate broader problems appropriately.

- Produce clear technical documentation, including data definitions, lineage, runbooks, and support notes.

- Participate in the team's on-call and incident response practices, with appropriate guidance and escalation support.

- Follow established security, privacy, governance, and access control requirements.

Collaboration & Growth

- Work with Data Analytics, Data Science, Software Engineering, IT, Security, and business partners to clarify requirements and deliver fit-for-purpose solutions.

- Break defined requirements into engineering tasks, communicate progress and risks, and deliver work within agreed timelines.