Intermediate Analytics Engineer

SUMMARY:
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.
 
Responsibilities:
Silver and gold modelling:

• 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.

• 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.

• 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.

• 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.

• Matric and a Bachelor's degree in Data Science, Statistics, Computer Science, Information Systems, Engineering or a related field, or equivalent practical experience.
• 3 or more years in analytics engineering, data engineering or advanced analytics.
• Expert SQL: window functions, CTEs, complex joins and large analytical models, with the judgment to tell a query that returns the right answer from one that will still return it on full SAP history.
• 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. The Databricks Certified Data Engineer Associate or the dbt Analytics Engineering certification also counts.
• 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.

• 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, with each ported model reconciled against its SAP source.
• Exposure to Power BI or Qlik and how BI tools consume analytical models.

POSITION INFO:
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. Responsibilities: Silver and gold modelling: 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. Qualifications and experience: Matric and a Bachelor's degree in Data Science, Statistics, Computer Science, Information Systems, Engineering or a related field, or equivalent practical experience. 3 or more years in analytics engineering, data engineering or advanced analytics. Expert SQL: window functions, CTEs, complex joins and large analytical models, with the judgment to tell a query that returns the right answer from one that will still return it on full SAP history. 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. The Databricks Certified Data Engineer Associate or the dbt Analytics Engineering certification also counts. 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 experience: 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, with each ported model reconciled against its SAP source. Exposure to Power BI or Qlik and how BI tools consume analytical models.