Senior AI Data Engineer
| Function: Data and AI | Manager: Data & AI Platform Engineering Manager | Location: Hybrid (Melbourne S.E. Suburbs) |
Role Purpose and the Impact You Can Make in This Role
The Senior AI Data Engineer is responsible for designing, building, and operating AI-ready data products and pipelines on the organisation's modern data platform.Reporting to the Data & AI Platform Engineering Manager, this role sits at the intersection of data engineering and applied AI. It focuses on ensuring that data is high-quality, well-modelled, and production-ready for advanced analytics, machine learning, and generative AI use cases.
This is a hands-on engineering role with strong influence over patterns, standards, and how AI is safely and effectively embedded into the organisation's data ecosystem.
Key Accountabilities
AI-Ready Data Engineering
- Design and build reliable, scalable data pipelines optimised for AI and advanced analytics use cases
- Develop high-quality feature sets and curated data models to support machine learning and GenAI workloads
- Ensure data products meet standards for:
- Training, inference, and experimentation
- Reproducibility and traceability
- Performance and cost efficiency
Applied AI & Advanced Analytics Enablement
- Partner with Analytics and Applied AI teams to support:
- Predictive and prescriptive analytics
- Machine learning models
- Generative AI and agent-based use cases
- Translate analytical and AI requirements into robust engineering solutions on Databricks
- Support model lifecycle needs, including:
- Feature generation
- Data refresh and versioning
- Monitoring and ongoing reliability
Modern Data Platform Contribution
- Build and maintain data assets on the Databricks Lakehouse, including:
- Structured, semi-structured, and streaming data
- Bronze / Silver / Gold data layers
- Contribute to platform patterns for:
- Data modelling
- CI/CD and automation
- Data quality and observability
- Collaborate closely with core platform engineers while remaining focused on AI-adjacent workloads
Data Quality, Governance & Safety
- Embed data quality checks, validation, and monitoring into all AI-relevant pipelines
- Ensure data products comply with:
- Security and privacy requirements
- Responsible AI and governance expectations
- Support lineage, documentation, and metadata capture to enable trust and auditability
AI-Assisted Engineering Practices
- Leverage AI-assisted development tools to improve engineering productivity
- Apply AI responsibly in coding, testing, and documentation, aligned with enterprise standards
- Contribute to evolving best practices for AI-assisted data engineering
Your Background and Education
Core Technical Experience
- Strong, hands-on experience as a senior data engineer in modern data environments
- Proven experience building AI-ready data pipelines and models
- Advanced SQL and strong Python skills
- Experience with Apache Spark / Databricks in production
- Any experience across other systems such as ECC6, SAP4Hana, are also highly valued
Cloud & Platform Experience
- Experience working on cloud-based data platforms — Azure and/or AWS experience highly valued
- Familiarity with:
- Lakehouse architectures
- Data modelling for analytics and ML
- Orchestration, CI/CD, and infrastructure-as-code concepts
- Experience integrating data platforms with analytics, ML, or AI tools
AI & Advanced Analytics Exposure
- Practical experience supporting:
- Machine learning models
- Feature engineering
- Advanced analytics or GenAI use cases
- Understanding of data requirements across the AI lifecycle (training ? inference ? monitoring)
- Awareness of AI risk, bias, and governance considerations in enterprise contexts
Ways of Working
- Strong problem-solving and systems thinking
- Comfortable working alongside data scientists, analysts, and platform engineers
- Pragmatic, delivery-focused mindset
- Clear communicator who can translate technical detail into business context
What Success Looks Like
Within 12–18 months, the Senior AI Data Engineer will have:- Delivered production-grade, AI-ready data products on the modern data platform
- Enabled faster, more reliable delivery of analytics and AI use cases
- Improved trust in data used for modelling, forecasting, and automation
- Established strong engineering patterns for applied AI workloads
- Become a recognised technical leader within the data engineering and AI community
