Data Support Engineer
About the role
We are seeking a mid-level Data Support Engineer (3-5 years of experience) to join our team. Working alongside internal specialists and client resources, this role balances operational system support with strategic development tasks across new and existing platforms. The successful candidate will work during UK business hours to maintain high system availability, resolve tickets efficiently, and extend metadata-driven data architectures.
What you'll do
- Deliver day-to-day operational support and incident resolution for client data systems under strict SLAs.
- Develop, maintain, and optimize dbt models, data pipelines, lakehouses, and warehouses within modern data environments.
- Debug, extend, and maintain existing metadata-driven PySpark ingestion frameworks at the staging layer.
- Build and support Power BI semantic models, reports, and star schemas with robust DAX measures and security configurations.
- Execute software deployments through version-controlled pipelines and maintain rigorous CI/CD branch/pull-request workflows.
- Create clear technical documentation, runbooks, incident write-ups, and handover notes for team and client use.
Requirements
- Microsoft Fabric. Hands-on experience of Fabric, or of the underlying components in their Azure Synapse or Databricks form: lakehouses, warehouses, notebooks, pipelines and workspace management. Candidates should understand the difference between a lakehouse and a warehouse in Fabric and when each is appropriate.
- dbt. Practical experience building and maintaining dbt models, sources, refs, tests, materialisations, documentation and lineage in a version-controlled project with more than one contributor.
- SQL. Strong SQL, including analytical functions, query tuning and dimensional modelling.
- Power BI. Competent semantic-model and report development: relationships and star schemas, DAX measures, row-level security, refresh configuration and gateway basics.
- Python and PySpark. Enough to read, debug and extend an existing metadata-driven PySpark ingestion framework. The role is not a Spark optimisation post, but the Stage layer is PySpark and the post holder must be able to work in it confidently.
- Git and CI/CD. Comfortable with branch and pull-request workflow and with deploying through a gated pipeline. Azure DevOps specifically is an advantage; the discipline matters more than the tool.
- Azure fundamentals. Working understanding of subscriptions, resource groups, Key Vault, managed identities and private networking concepts sufficient to tell a platform fault from an application fault and to escalate it accurately.
About Cloud Employee
Cloud Employee, is a UK-owned business established 8 years ago. We connect high-performing software engineer talent worldwide with some of the world’s leading and most innovative tech companies. Developers join to work as part of international engineering teams and grow their CV and skill set. We pride ourselves on being a supportive and cutting-edge workplace that continuously invests in staff development, engagement, and well-being. We provide security, and career paths, along with individual training programs and mentoring.
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