Data Engineer

Clearai; City of Brisbane; Hybrid; AUD 110,000 - 130,000; Full time

Job summary

ClearAI is seeking a Data Engineer who would rather build something new than wait for the next ticket. Our clients operate real-world systems, and your role is to turn messy data into actionable insights for leaders to act on.

You’ll build AI-enabled data workflows, validate models, and present outcomes clearly to executives, with a focus on impact and real-time decision support.

Qualifications

• Experience turning raw client data into structured, usable datasets.
• Ability to frame analyses around business outcomes.
• Comfort with analyzing incomplete/dirty data.

Responsibilities

• Get to the answer, whatever state the data is in.
• Structure, reconcile, and extract value from clients' raw data.
• Think like a scientist: form hypotheses, test them, report results.
• Build workflows that integrate AI into the analytic process.
• Deliver real-time insights to Executives and field operators.

Skills

Data engineering

AI/ML thinking

Statistical testing

Model validation

Job description

The harder path is the one that matters.

At ClearAI, we believe AI must be about more than performance. We work with courageous leaders to adopt artificial intelligence responsibly: building governance that protects people, capability that empowers teams, and technology that creates lasting value. Not hype. Not automation at all costs. A future worth inheriting.

We're growing, and we're looking for a Data Engineer who would rather build something new than wait for the next ticket.

The Role

Our clients run real, physical operations, and their data arrives the way real data does: sensor readings and historian exports, SharePoint folder sprawl, hand-filled logsheets, finance extracts that follow a naming convention of their own, and thousands of pages of engineering drawings. Your job is to turn that into answers that leaders act on.

We also want to show that an engineer working properly with AI can do what a small team used to do. This isn't a seat in a large team of specialists working through a queue. You'll have a blank canvas and the backing to use it. The tools, workflows and methods we rely on for years to come are still being invented, and you'll be one of the people inventing them. Ideas move from whiteboard to client work in weeks, not quarters, and what you build here gets used.

What You'll Do

Get to the answer, whatever state the data is in.

You’ll take a client's raw operational data, structure it, reconcile it, and find what's hiding in it. You'll treat documents as a data source, not just attachments.

Think like a scientist.

You'll form hypotheses, test several of them, and report what you find. You'll check how accurate your models are, and you'll know the difference between correlation and causation, and between variance and trend. When a difference is inside the noise, or the data can't support the question being asked, you'll say so plainly.

Build with AI.

AI is part of how you actually work, not a side interest. You'll find the parts of the analytical process that an agent should run instead of a person, build those workflows, and put a check on their output.

Leave something behind.

You’ll build apps that take your analysis and deliver it in real time insights to Executives and field operators.

Speak the client's language.

A regression is only useful if it changes a decision. You'll frame your analysis around client outcomes and return on investment, including how the change will be measured. You'll be able to explain a number in two sentences to the person who has to act on it. You'll work alongside our Engagement Managers and sometimes directly with clients.

Who You Are

Maybe you're a few years into a quantitative career and already building things with AI that your workplace hasn't caught up to. Maybe you're a computer science graduate who has been shipping your own apps and tools on the side. Or maybe you've spent years, even decades, in data, and you're ready to trade maintaining systems for building new ones. We care less about the path and more about who you are and what you'll bring.

Whatever your background, you've done real analysis on data that wasn't clean. You'll likely have studied computer science, engineering, statistics, economics or science.. You use AI heavily in your actual work, and you've shipped things with it rather than only reading about it. You understand statistical testing and model validation, and you're comfortable working at scale.

You're creative, optimistic, inquisitive, and a self-starter. When a new tool appears that might do the job better, you spend the weekend learning it and take the risk on it. You're curious about how a business physically runs.

What We Don't Expect on Day One

You don't need to arrive knowing Microsoft Fabric and Power BI, semantic modelling, or Git-based workflows. We'll help you get there quickly. We're hiring for judgement, curiosity and drive.

This Probably Isn't for You If

• You need clean, modelled data before you can start.
• You'd prefer no contact with clients or the business behind the numbers.
• You're most comfortable with a clearly defined task list and frequent check-ins.

The Practical Stuff

• Location: Brisbane or Melbourne. We work from the office five days per week. If you're based outside these cities, we're open to discussing remote arrangements for the right person.
• Travel: Occasional client site visits, including regional and industrial sites.
• Salary: $110,000–$130,000 per year plus superannuation, commensurate with experience.
• Employment type: Full-time, permanent.