Data Engineer

About the role

The Data Engineer will support MARSS’s machine‑learning initiatives within Electro Optic Systems (EOS) by building, scaling and maintaining data pipelines that feed AI models deployed in defence and space missions. Working closely with AI engineers, software teams and field operators, you will ensure that data is reliable, available and ready for advanced analytics.

Key responsibilities

• Design and implement scalable data collection pipelines for distributed deployments.
• Guarantee reliability, completeness and performance of data recording systems across multiple sites.
• Manage large‑scale, distributed datasets and maintain structured storage solutions.
• Monitor data integrity and system health, addressing anomalies and performance issues.
• Support data annotation workflows and tooling for machine‑learning projects.
• Extract and analyse metadata such as system usage, anomalies and performance indicators.
• Prepare datasets for classification, tracking and prediction tasks.
• Ensure compliance with data regulations in various jurisdictions.
• Collaborate with cross‑functional teams to improve data accessibility and usability.

Required profile

• Bachelor’s degree in Engineering, Computer Science or a related field (Master’s/PhD a plus).
• Proven experience with large‑scale or distributed data systems in Linux environments.
• Strong background in Python, Bash and containerisation (Docker, Docker Compose).
• Advanced SQL skills and experience designing structured datasets.
• Familiarity with data pipeline design, data lifecycle management and exposure to machine‑learning workflows.

Required skills

• Python
• Bash
• Docker
• Docker Compose
• SQL
• Linux
• Data pipeline design
• Data lifecycle management
• TensorFlow (optional)
• PyTorch (optional)