Data Engineer

Atlas Air•Miami, FL

8d•$73,500 - $99,500

About The Position

Atlas Air is seeking an early-career individual contributor to join its Data Platform and Engineering team. The engineer will deliver defined data solutions with guidance, build engineering fundamentals, and gradually take ownership of more complex work. As Atlas Air develops its data environment on Databricks, this role will help build and operate reliable data pipelines, transformations, and data models. The engineer will work within established architecture and standards, collaborate with experienced engineers, and write production-quality code supporting analytics, data science, AI/ML, and operational decision-making.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related quantitative field, or equivalent practical experience.
At least 1 year of professional experience in data engineering, software engineering, analytics engineering, or a closely related field.
Working proficiency in SQL and at least one general-purpose programming language, such as Python, TypeScript, Rust, Java, or C++.
Experience building or supporting data pipelines, transformations, or data models in a professional, internship, academic, or substantial project setting.
Familiarity with ETL/ELT, relational data modeling, data quality testing, and pipeline orchestration.
Experience with Git-based development practices and an understanding of code review, automated testing, and deployment workflows.
Ability to troubleshoot technical issues methodically, communicate clearly, and collaborate with technical and non-technical partners.
Must be legally authorized to work in the United States; employment visa sponsorship and employer immigration support are not available now or in the future.

Nice To Haves

Hands-on exposure to Databricks, Apache Spark, or another distributed data-processing platform.
Experience with dbt or a similar transformation framework and an orchestration tool such as Airflow, Prefect, or Dagster.
Familiarity with AWS, Azure, or GCP and its data services.
Exposure to streaming or event-driven technologies such as Kafka, Kinesis, Spark Structured Streaming, or Flink.
Familiarity with BI, data science, or machine learning workflows and the data products that support them.
Experience in aviation, transportation, logistics, or another operationally complex industry.

Responsibilities

Build, test, deploy, and maintain batch and streaming data pipelines for structured and semi-structured data sources using established patterns and frameworks.
Develop transformations that create accurate, documented, 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.
Create automated tests, validation checks, logging, and monitoring for developed pipelines and datasets.
Investigate data quality, reliability, and performance issues; resolve routine issues independently and escalate broader problems appropriately.
Produce 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.
Collaborate with Data Analytics, Data Science, Software Engineering, IT, Security, and business partners to clarify requirements and deliver suitable solutions.
Break defined requirements into engineering tasks, communicate progress and risks, and deliver work within agreed timelines.
Contribute to team standards for source control, testing, CI/CD, documentation, and operational readiness.
Learn the Atlas Air business domain and apply that understanding to improve the usefulness and reliability of data products.
Seek feedback, share knowledge, and progressively take ownership of larger components and less-defined problems.