Data & AI Engineer/Analyst

We’re looking for data people who like building things.

People who understand that good AI starts with good data, and who enjoy getting into complex environments, working out how information needs to move, connect and be accessed, and building the foundations that make everything else possible.

Sense is working exclusively with a large Australian organisation making a significant investment in its enterprise AI capability, and we’re helping them build the team to take it forward. There’s a lot happening, plenty still to be shaped and a genuine appetite to get moving.

This isn’t about maintaining a reporting environment or building another dashboard.

The organisation is looking at how enterprise data can be made available to AI securely and responsibly, how different sources can be connected and how the right data foundations can be established to support LLMs, RAG, AI agents, automation and new AI-enabled solutions across the business.

We’re looking for people who can help build that.

You might be a Data Engineer, Data & AI Engineer, Analytics Engineer or someone whose career has started in modern data platforms and is moving increasingly into AI.

You’ll work closely with AI engineers, architects, security, infrastructure and the business to understand what data exists, where it sits, how it needs to be accessed and what needs to happen to make it genuinely useful.

You’ll be comfortable getting hands-on. Building pipelines, integrating data sources, working with APIs, transforming and modelling data and solving the messy problems that come with real enterprise information.

And importantly, you’ll understand that making data available to AI doesn’t mean making everything available to everyone.
Security, identity, permissions, data quality, lineage, governance and sensitive information all matter here. We need people who understand how to build capability without losing sight of the controls around it.
Who are we looking for? We’re interested in strong data people from modern engineering environments.

You might have experience across Azure, Databricks, Microsoft Fabric, Snowflake or similar modern data platforms, with strong SQL and ideally Python capability. You may have worked with lakehouse architectures, ETL/ELT, APIs, streaming, data modelling, orchestration or enterprise integration.
If you’ve already worked with vector databases, embeddings, RAG, LLMs, unstructured data, AI pipelines or Agentic AI, we definitely want to hear about it.

But you don’t need to have spent the last five years calling yourself an AI Engineer.

If you’re technically strong, curious about AI and understand data well enough to help solve the challenges underneath it, your experience could be highly relevant.