Data Engineer, Airships to Cloud

The role is about keeping the whole data path working, from the sensors on the airship to the cloud pipelines, and then making each step cheaper and faster. A strong candidate is a generalist who can debug a payload on the bench one day and trim cloud costs the next. Solid Python and Linux skills matter, along with a record of running something in production and fixing it when it broke.

How it fits the AI era

AI tools handle much of the routine coding, and the role expects using them to automate data checks, incident triage and reporting. The company's own AI turns the flight data into defect findings, so the quality and completeness of what reaches it directly shapes results. What stays human is judging why data went missing, working out hardware and network faults in the field, and deciding what is worth keeping or dropping.

How it builds AI experience

The work sits right beneath a defect detection system, so a hire would see closely how good data feeds a working AI product built on LiDAR, imagery and methane readings. Hands-on practice with coding assistants and agents on real operational workflows is part of the day to day. To build toward it, try automating a data quality check or an incident triage flow on a small sensor dataset.

Worth showing

• Python and Linux scripting
• Cloud storage and pipelines
• Embedded or field hardware debugging
• Runbooks and monitoring built

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About HyLight

Autonomous, hydrogen-powered airships for zero-emission aerial inspection of critical energy and transport grids.