Lead Data Engineer - Azure & Databricks Lakehouse
Glasgow (3/4 days onsite) | Exclusive Role with a Leading UK Consumer Business
A rapidly scaling UK consumer brand is undertaking a major data modernisation programme-moving away from legacy systems, manual Excel reporting and fragmented data sources into a fully automated Azure Enterprise Landing Zone + Databricks Lakehouse.
They are building a modern data platform from the ground up using Lakeflow Declarative Pipelines, Unity Catalog, and Azure Data Factory, and this role sits right at the heart of that transformation.
This is a rare opportunity to join early, influence architecture, and help define engineering standards, pipelines, curated layers and best practices that will support Operations, Finance, Sales, Logistics and Customer Care.
If you want to build a best-in-class Lakehouse from scratch-this is the one.
? What You'll Be Doing
Lakehouse Engineering (Azure + Databricks)
Engineer scalable ELT pipelines using Lakeflow Declarative Pipelines, PySpark, and Spark SQL across a full Medallion Architecture (Bronze ? Silver ? Gold).
Implement ingestion patterns for files, APIs, SaaS platforms (e.g. subscription billing), SQL sources, SharePoint and SFTP using ADF + metadata-driven frameworks.
Apply Lakeflow expectations for data quality, schema validation and operational reliability.
Curated Data Layers & Modelling
Build clean, conformed Silver/Gold models aligned to enterprise business domains (customers, subscriptions, deliveries, finance, credit, logistics, operations).
Deliver star schemas, harmonisation logic, SCDs and business marts to power high-performance Power BI datasets.
Apply governance, lineage and fine-grained permissions via Unity Catalog.
Orchestration & Observability
Design and optimise orchestration using Lakeflow Workflows and Azure Data Factory.
Implement monitoring, alerting, SLAs/SLIs, runbooks and cost-optimisation across the platform.
DevOps & Platform Engineering
Build CI/CD pipelines in Azure DevOps for notebooks, Lakeflow pipelines, SQL models and ADF artefacts.
Ensure secure, enterprise-grade platform operation across Dev ? Prod, using private endpoints, managed identities and Key Vault.
Contribute to platform standards, design patterns, code reviews and future roadmap.
Collaboration & Delivery
Work closely with BI/Analytics teams to deliver curated datasets powering dashboards across the organisation.
Influence architecture decisions and uplift engineering maturity within a growing data function.
? Tech Stack You'll Work With
Databricks: Lakeflow Declarative Pipelines, Workflows, Unity Catalog, SQL Warehouses
Azure: ADLS Gen2, Data Factory, Key Vault, vNets & Private Endpoints
Languages: PySpark, Spark SQL, Python, Git
DevOps: Azure DevOps Repos, Pipelines, CI/CD
Analytics: Power BI, Fabric
? What We're Looking For
Experience
5-8+ years of Data Engineering with 2-3+ years delivering production workloads on Azure + Databricks.
Strong PySpark/Spark SQL and distributed data processing expertise.
Proven Medallion/Lakehouse delivery experience using Delta Lake.
Solid dimensional modelling (Kimball) including surrogate keys, SCD types 1/2, and merge strategies.
Operational experience-SLAs, observability, idempotent pipelines, reprocessing, backfills.
Mindset
Strong grounding in secure Azure Landing Zone patterns.
Comfort with Git, CI/CD, automated deployments and modern engineering standards.
Clear communicator who can translate technical decisions into business outcomes.
Nice to Have
Databricks Certified Data Engineer Associate
Streaming ingestion experience (Auto Loader, structured streaming, watermarking)
Subscription/entitlement modelling experience
Advanced Unity Catalog security (RLS, ABAC, PII governance)
Terraform/Bicep for IaC
Fabric Semantic Model / Direct Lake optimisation
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