Most job ads for data engineers talk about “exciting opportunities” and “fast-paced environments.” Translation: you’ll be cleaning up spaghetti pipelines for the next two years.

This role isn’t that.

You’ll be the one setting the direction. The one deciding how data gets used, stored, and scaled. The one whose work powers AI models, analytics, and commercial decisions, not just another person duct-taping together broken queries.

Here’s what you’ll actually be doing:

• Building pipelines in Azure that don’t collapse under their own weight.
• Designing data architecture that can scale without burning money.
• Creating infrastructure that makes machine learning models work in the real world.
• Partnering with product and commercial teams so data isn’t just “available” but useful.
• Putting proper governance and observability in place

What you need to bring:

• Strong Azure knowledge: Data Factory, Synapse, Event Hubs, Functions, Storage.
• Databricks, Elasticsearch, Python or .Net
• A handle on data modelling, governance, and security that goes beyond theory.
• Communication skills that don’t make commercial people back slowly out of the room.

Extra brownie points if you’ve:

• Worked with Snowflake, Airflow, Kafka, MLflow, Feature Stores.
• Built AI or propensity models that actually got used.
• Seen the inside of a SaaS or startup and lived to tell the tale.

What’s in it for you:

• Flexible hours and remote work – no surprises there.
• The chance to build a data strategy from scratch, not inherit one from 2012.
• A culture where you won’t be punished for learning.
• Work that directly influences the success of the product

This isn’t a role for someone who wants to be told what to build. It’s for someone who wants to shape the whole data landscape.

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