Work at the forefront of AI innovation

Collaborate with forward thinking AI enthusiasts

Continuous learning in one of the fastest-moving fields in tech

Our people are customer obsessed. They prioritise the needs and satisfaction of the customer above all else.  Our mindset fosters innovation and creates strong, lasting customer relationships as we strive to be the most customer centric company in Australia and New Zealand

In the role of Solution Designer, you will join the Data and Analytics team that’s doubling down on customer obsession.

In this Solution Designer role, you will:

• Design end-to-end solutions to support GenAI use case experiments and pilots.
• Design, develop, test, deploy and maintain data pipelines, data marts and warehouse assets.
• Develop flowcharts, layouts and documentation to identify requirements & solutions
• Perform data governance and identify data gaps.
• Identify key insights from data and ensure data model meets client requirements.
• Produce assessments and determine operational feasibility
• Integrate APIs, microservices, serverless components and event-driven patterns

We’re looking for the best and brightest to deliver the best for our customers. You’ll need:  

• Designing end‑to‑end GenAI workflows and agentic solutions that span from experimentation to scalable production deployment.
• Applying prompt engineering, RAG patterns, vector database integration and orchestration frameworks.
• Using ML Ops and LLM Ops frameworks such as MLflow, LangChain and Databricks Model Serving.
• Hands on cloud‑based design configuration and development experience with AWS and Azure.
• Building scalable data engineering solutions including data pipelines on Databricks or Delta Lake, and data extraction across multiple sources.
• Programming in Python, SQL, Unix Shell, PySpark and TypeScript/JavaScript, with strong knowledge of data modelling and Power BI.

• Design end-to-end solutions to support GenAI use case experiments and pilots.
• Design, develop, test, deploy and maintain data pipelines, data marts and warehouse assets.
• Develop flowcharts, layouts and documentation to identify requirements & solutions
• Perform data governance and identify data gaps.
• Identify key insights from data and ensure data model meets client requirements.
• Produce assessments and determine operational feasibility
• Integrate APIs, microservices, serverless components and event-driven patterns

• Designing end‑to‑end GenAI workflows and agentic solutions that span from experimentation to scalable production deployment.
• Applying prompt engineering, RAG patterns, vector database integration and orchestration frameworks.
• Using ML Ops and LLM Ops frameworks such as MLflow, LangChain and Databricks Model Serving.
• Hands on cloud‑based design configuration and development experience with AWS and Azure.
• Building scalable data engineering solutions including data pipelines on Databricks or Delta Lake, and data extraction across multiple sources.
• Programming in Python, SQL, Unix Shell, PySpark and TypeScript/JavaScript, with strong knowledge of data modelling and Power BI.