Asset Integration Engineer (Aviation Data & Analytics)

Airbus•Atlanta, GA

4d•Hybrid

About The Position

Skywise is a leading digital services company, wholly owned by Airbus. We deliver a first-of-its-kind offer that seamlessly integrates Planning & Operations Control, Flight, Technical, and Ground Operations. By providing interoperable digital solutions for end-to-end operations, Skywise combines aircraft manufacturer expertise and digital know-how to transform operational complexity into predictable and profitable performance, ensuring resilient and more sustainable operations. As an Asset Integration Engineer, you will be the technical bridge between Skywise and global partners like Delta TechOps, GE Aerospace, Collins Aerospace and Liebherr. You will help them integrate their expert predictive models into Skywise, the backbone of Airbus data infrastructure, to make global aviation safer and more sustainable.

Requirements

Basic understanding of aircraft systems, the types of parameters they generate, and the sensors (temperature, vibration, pressure) used to monitor flight health.
Basic experience (or an MS equivalent) with coding, preferably: Python, SQL, and Regex.
Ability to look at a data set and understand what stands behind them.
Proficiency in English to explain complex technical solutions to both fellow engineers and non-technical stakeholders.
Bachelor's or Master's degree in Computer Science, Data Science or related field, or equivalent professional experience.
Minimum 2+ years experience (or MS equivalent).
Capability to propose new opportunities and solutions (e.g., new AI/ML possibilities, tech architecture).
Ability to research autonomously in R&D topics.

Nice To Haves

Excited to master tools like Palantir Foundry, Apache Spark (PySpark), and specialized python frameworks which stand behind skywise platform.

Responsibilities

Build and improve the model integration pipeline, focusing on automating data workflows and enabling efficient data integration.
Serve as the support for partners, helping them retrieve aircraft data (parameters, logbooks) and integrate their maintenance models into our ecosystem.
Automate data workflows to transform disparate datasets into "high-signal" models.
Autonomously research R&D topics, proposing new AI/ML possibilities and technical architectures to optimize scaling and cost.
Participate in weekly discussions with international partners and managers, occasionally traveling to collaborate with global teams.
Participate in technical discussions, documentation needs, research, and innovation and sustainability topics.