Intermediate Analytics Engineer

The Intermediate Analytics Engineer builds the silver and gold layers the business runs on: canonical models and certified metrics in SQL on Databricks, with the tests and documentation that keep them trustworthy. Gold is the product. Dashboards, analysts, applications and AI agents all consume the same certified tables and metric views, so every measure is defined once and used everywhere.

WHAT WE'RE LOOKING FOR?

Minimum

• Bachelor's degree in Data Science, Statistics, Computer Science, Information Systems, Engineering or a related field, or equivalent practical experience.
• 3 to 5 years in analytics engineering, data engineering or advanced analytics.
• Expert SQL: window functions, CTEs, complex joins and large analytical models.
• Strong data modelling: star schemas, dimensional modelling, fact and dimension design.
• Experience with a declarative transformation framework: Databricks Lakeflow Declarative Pipelines, or dbt, which transfers directly.
• Git and CI/CD in data work: pull requests, review and automated deployment. The Databricks Git UI counts.
• Working SAP knowledge: BW objects such as InfoProviders, extractors and routines, plus S/4HANA or Business Data Cloud. This role reads SAP semantics daily during the BW-to-Databricks migration.

Advantageous

• Retail data, ideally pharmacy retail. Healthcare or financial services also count.
• Working Python for pipeline unit tests and transformations SQL cannot express.
• Coding agents such as Claude Code or Codex: turns repetitive migration work into automated, repeatable steps, each ported model reconciled against its SAP source.
• Exposure to Power BI or Qlik and how BI tools consume analytical models.

WHAT YOU WILL BE DOING?

• Build silver and gold models in Databricks using Lakeflow Declarative Pipelines and dimensional modelling so that the business queries one consistent, fast layer.
• Define certified metrics in gold tables and Unity Catalog metric views by agreeing definitions with metric owners so that every consumer, human or AI agent, reports the same number.
• Maintain one authoritative definition per metric, with owner, grain, lineage and agent metadata such as display names and synonyms, so that certification is auditable and BI tools and AI agents read measures correctly.
• Add expectations and data quality checks to every model by codifying rules in the pipeline so that failures are caught before consumers see them.
• Ship changes through Git pull requests, GitHub Actions and Databricks Asset Bundles so that every change is reviewed, tested and reversible.

WHO YOU ARE?

• Have a knowledge of how retail business processes turn into facts, dimensions and metrics, and how the SAP estate maps onto the medallion layers.
• Be able to find the root cause of data problems and fixes them once, in the model.
• Be able to explain trade-offs to technical and business audiences.
• Owns delivery of own models end to end and manages competing priorities.
• Manages own backlog and commitments; no people management.