Studytube makes professional development accessible, easy and actually enjoyable. Behind that platform sits a growing stream of learning data, and we want to put it to work for our customers, our product teams, and the AI-powered features we are building next.

We are looking for a Data Engineer who likes ownership. You will be the engineering backbone of a small data team: you turn messy source data into models people trust, and you leave the platform better than you found it.

**The role**
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You join the Product \& Tech team as the data engineer in a team of three, working side by side with data analysts. The team will grow. Over time a dedicated data team with a Data Lead is likely. For now, you report to the Head of Engineering and you own our data platforms end to end.

That means real scope. You design, build and run the pipelines that take raw product and business data through to the marts that power analytics, reporting and product features. You talk to analysts, product managers and engineers, translate what the business needs into something that actually works, and keep quality non-negotiable: tests, monitoring and documentation are part of the job.

Part of our platform already runs on Databricks: a dedicated Azure environment for customers with strict security requirements. You will take ownership of it, working alongside the external specialists who built it until you run it independently. Next, you will help prepare our main warehouse move from Snowflake towards Databricks.

**What you will do**
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* Own our main Snowflake warehouse: ingestion, transformation, modelling and the marts analysts and product teams rely on.

* Own our Databricks environment on Azure: pipelines, access, cost and daily operations, in a setup with strict security and compliance requirements.

* Build end-to-end pipelines that are incremental where it matters, idempotent, tested and documented.

* Bring structure to what we have today: clean up the current architecture, tighten conventions and make the platform easier to extend.

* Ingest new sources via Fivetran or custom connectors, including the quirks that never make it into the docs.

* Run dbt Cloud jobs with clear dependencies, scheduling and failure handling.

* Monitor freshness, volume and schema drift, and do a proper root-cause analysis when something breaks.

* Help Studytube measure what matters: support the implementation of use-case metrics the business can actually act on.

* Work GDPR-aware: access, sensitivity, retention and deletion are part of how you design.