What we're building:

We're a small team building AI-powered solutions for real enterprise problems — autonomous agents, intelligent workflows, and the data infrastructure that makes them work. We work on Microsoft Azure and Fabric, and we move fast.

This is an early-career role with a real growth path. You'll start on the data layer — pipelines, lakehouses, connecting enterprise systems — and grow into understanding AI systems, business processes, and eventually how solutions are designed and delivered.

What you'll do:

Build and maintain data pipelines on Microsoft Fabric — ingesting data from enterprise systems into OneLake, keeping it clean, structured, and reliable.

Set up and manage Lakehouse structures — understand what data exists, what it means, and how it should be organised for the systems that consume it.

Support the AI and agent layer — work with the engineering team to make sure the right data reaches the right agent at the right time, in the right shape.

Build dashboards and reports in Power BI that give stakeholders real visibility into what's happening operationally.

Get close to the problem — understand why the data exists, not just how to move it. Ask the questions that help the team build the right thing.

Take ownership — pipelines break, data goes missing, things go wrong at the wrong time. You're the person who notices, fixes it, and tells the team what happened.

What we're looking for:

Hands-on experience with Microsoft Fabric — you've worked on at least one real project involving Fabric pipelines, Lakehouse, or Power BI. You're not an expert but you're not starting from zero.

Data literacy — you can look at a schema or a dataset and understand what the business is doing. You notice when something looks wrong without being told to check.

Curiosity about the problem, not just the task — you ask what the data will be used for. You want to understand the system, not just your part of it.

Ownership mentality — you don't wait to be told when something is broken. You don't close a ticket without making sure the problem is actually solved.

Clear communication — you can explain a data issue to someone non-technical without losing them. You can write a clear summary of what you found and what you did about it.

Willingness to learn fast — this project uses Azure AI Foundry, agent frameworks, RAG pipelines. You haven't necessarily touched these. You will. Fast.

Experience Range: 8-10 yrs

Engagement: 3-month project-based consulting role, with a possibility of extension based on client requirements and project needs.

Good to have:

Exposure to Azure services — Data Factory, Event Hub, Azure AI Search, Cosmos DB.

Some Python or SQL beyond the basics — enough to debug a pipeline or transform data when a no-code tool doesn't cut it.

Experience in a client-facing or consulting environment — you've had to explain technical work to people outside the team.

Any exposure to AI projects — even at the edges. You've seen how an LLM or agent consumes data and have a sense of what "good data for AI" looks like.

Data & AI Platform Associate (MS Fabric)

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