Data Engineer

Mid-Level Data Engineer (Azure / Databricks)

NO VISA REQUIREMENTS

Location: Glasgow (3+ days)
Reports to: Head of IT
My client is undergoing a major transformation of their entire data landscape-migrating from legacy systems and manual reporting into a modern Azure + Databricks Lakehouse. They are building a secure, automated, enterprise-grade platform powered by Lakeflow Declarative Pipelines, Unity Catalog and Azure Data Factory.
They are looking for a Mid-Level Data Engineer to help deliver high-quality pipelines and curated datasets used across Finance, Operations, Sales, Customer Care and Logistics.

What You'll Do

Lakehouse Engineering (Azure + Databricks)

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  Build and maintain scalable ELT pipelines using Lakeflow Declarative Pipelines, PySpark and Spark SQL.
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  Work within a Medallion architecture (Bronze ? Silver ? Gold) to deliver reliable, high-quality datasets.
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  Ingest data from multiple sources including ChargeBee, legacy operational files, SharePoint, SFTP, SQL, REST and GraphQL APIs using Azure Data Factory and metadata-driven patterns.
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  Apply data quality and validation rules using Lakeflow Declarative Pipelines expectations.

Curated Layers & Data Modelling

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  Develop clean and conforming Silver & Gold layers aligned to enterprise subject areas.
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  Contribute to dimensional modelling (star schemas), harmonisation logic, SCDs and business marts powering Power BI datasets.
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  Apply governance, lineage and permissioning through Unity Catalog.

Orchestration & Observability

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  Use Lakeflow Workflows and ADF to orchestrate and optimise ingestion, transformation and scheduled jobs.
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  Help implement monitoring, alerting, SLAs/SLIs and runbooks to support production reliability.
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  Assist in performance tuning and cost optimisation.

DevOps & Platform Engineering

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  Contribute to CI/CD pipelines in Azure DevOps to automate deployment of notebooks, Lakeflow Declarative Pipelines, SQL models and ADF assets.
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  Support secure deployment patterns using private endpoints, managed identities and Key Vault.
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  Participate in code reviews and help improve engineering practices.

Collaboration & Delivery

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  Work with BI and Analytics teams to deliver curated datasets that power dashboards across the business.
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  Contribute to architectural discussions and the ongoing data platform roadmap.

Tech You'll Use

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  Databricks: Lakeflow Declarative Pipelines, Lakeflow Workflows, Unity Catalog, Delta Lake
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  Azure: ADLS Gen2, Data Factory, Event Hubs (optional), Key Vault, private endpoints
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  Languages: PySpark, Spark SQL, Python, Git
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  DevOps: Azure DevOps Repos & Pipelines, CI/CD
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  Analytics: Power BI, Fabric

What We're Looking For

Experience

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  Commercial and proven data engineering experience.
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  Hands-on experience delivering solutions on Azure + Databricks.
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  Strong PySpark and Spark SQL skills within distributed compute environments.
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  Experience working in a Lakehouse/Medallion architecture with Delta Lake.
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  Understanding of dimensional modelling (Kimball), including SCD Type 1/2.
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  Exposure to operational concepts such as monitoring, retries, idempotency and backfills.

Mindset

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  Keen to grow within a modern Azure Data Platform environment.
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  Comfortable with Git, CI/CD and modern engineering workflows.
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  Able to communicate technical concepts clearly to non-technical stakeholders.
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  Quality-driven, collaborative and proactive.

Nice to Have

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  Databricks Certified Data Engineer Associate.
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  Experience with streaming ingestion (Auto Loader, event streams, watermarking).
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  Subscription/entitlement modelling (e.g., ChargeBee).
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  Unity Catalog advanced security (RLS, PII governance).
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  Terraform or Bicep for IaC.
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  Fabric Semantic Models or Direct Lake optimisation experience.

Why Join?

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  Opportunity to shape and build a modern enterprise Lakehouse platform.
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  Hands-on work with Azure, Databricks and leading-edge engineering practices.
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  Real progression opportunities within a growing data function.
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  Direct impact across multiple business domains.