Chief Data Architect

2 weeks ago

Melbourne, Victoria, Australia beBeeData Full time $140,000 - $180,000

Job Opportunity: Data Expert

We are developing cutting-edge AI models in Australia, from the ground up. Our foundation models necessitate large-scale datasets, designed and built by our team.

As a Senior Data Engineer, you will spearhead the design and construction of these datasets, working hands-on to source, clean, transform, and structure massive amounts of raw data into training-ready form.

You will design the architecture that powers data ingestion, validation, and storage for multi-terabyte to petabyte-scale AI training, collaborating with AI Researchers and Engineers to ensure every byte is high quality, relevant, and optimized for training cutting-edge large language models and other architectures.

This is a deep technical role, requiring expertise in data science, systems engineering, and software development. You will write code, build pipelines, define schemas, and debug unusual data edge cases at scale.

  • Design and build large-scale data ingestion and curation pipelines for AI training datasets.
  • Source, filter, and process diverse data types including text, structured data, code, and multimodal, from raw form to model-ready format.
  • Implement robust quality control and validation systems to ensure dataset integrity, relevance, and ethical compliance.
  • Architect storage and retrieval systems optimized for distributed training at scale.
  • Build tooling to track dataset lineage, reproducibility, and metadata at all stages of the pipeline.
  • Work closely with AI Researchers to align datasets with evolving model architectures and training objectives.
  • Collaborate with DevOps and ML engineers to integrate data systems into large-scale training workflows.
Required Skills and Qualifications:
  • Strong background in data science, systems engineering, and software development.
  • Expertise in designing and building large-scale data ingestion and curation pipelines.
  • Ability to source, filter, and process diverse data types.
  • Proficiency in implementing robust quality control and validation systems.
  • Knowledge of architecting storage and retrieval systems for distributed training.
  • Experience with building tooling to track dataset lineage and metadata.
  • Collaborative mindset to work with cross-functional teams.

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