7 Aug 2026

Data Engineer

Arkeus

Port Melbourne

7 Aug 2026

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Data Engineer

Arkeus is seeking a Data Engineer to own the pipelines that turn raw imagery and video from optical sensing platforms into clean, well-structured data for the Machine Learning team. The role involves building and maintaining ETL pipelines, conducting exploratory data analysis, and ensuring data is ML-ready. Requires 3+ years of experience, strong SQL skills, and proficiency in Python.

Core AIOn-siteFull-timeMid LevelSQLPython

Data Engineer

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Core AIOn-siteFull-timeMid LevelSQL

Salary

Not specified

Work Location

Port Melbourne, Victoria, Australia, AU

Work Model

On-site

Experience Required

3 years

Employment Type

Full-time

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)

SQLPythonETLPandasMatplotlibExploratory Data AnalysisData VisualizationUnstructured DataSemi-structured DataData QualityData LabelingData VersioningAnnotation ToolsCloud StorageBigQuery

Soft Skills

CollaborationCommunicationProblem SolvingPrototypingAttention to Detail

Preferred Qualifications

Technical (Nice-to-have)

Computer VisionMachine LearningOpenCVS3BigQuery

Key Responsibilities

•Design, build, and maintain ETL pipelines for large volumes of imagery and video data.
•Conduct exploratory data analysis to understand dataset quality, coverage, and gaps.
•Clean, structure, and organise datasets so they're readily usable by the ML team.
•Build fast prototype visualisations to surface data issues and communicate findings to the team.
•Write and optimise SQL to query, transform, and manage data at scale.
•Collaborate closely with ML engineers to understand what 'training-ready' data actually requires.
•Maintain documentation of datasets, pipelines, and data quality processes.

Data EngineeringETLSQLPythonMachine LearningDefenseSpace ManufacturingOn-siteFull-timeMid Level

Key Responsibilities

•Design, build, and maintain ETL pipelines for large volumes of imagery and video data.

•Conduct exploratory data analysis to understand dataset quality, coverage, and gaps.

•Clean, structure, and organise datasets so they're readily usable by the ML team.

•Build fast prototype visualisations to surface data issues and communicate findings to the team.

•Write and optimise SQL to query, transform, and manage data at scale.

•Collaborate closely with ML engineers to understand what 'training-ready' data actually requires.

•Maintain documentation of datasets, pipelines, and data quality processes.