Intermediate Analytics Engineer

Summary of Role

The 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. Dashboards, analysts, applications and AI agents all consume the same certified tables and metric views, so every measure is defined once and used everywhere.

• Build silver and gold models in Databricks using Lakeflow Declarative Pipelines and dimensional modelling so that the business queries one consistent, fast layer.
  Keep every business rule, join and aggregation inside the models, leaving consuming tools with none of their own, so that each rule is defined once and used everywhere.
  

Metrics and Semantics

• 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, so that certification is auditable and BI tools and AI agents read measures correctly.
  

Quality, Testing and Migration Assurance

• Add expectations and data quality checks to every model by codifying rules in the pipeline so that failures are caught before consumers see them.
  
• Reconcile Databricks outputs against SAP BW extracts during the migration so that migrated reports are trusted.
  

Delivery and Governance

• Ship changes through Git pull requests, GitHub Actions and Databricks Asset Bundles so that every change is reviewed, tested and reversible.
  
• Apply access control, masking and row filters aligned to data sensitivity so that governed self-service stays safe.