Senior Analytics Engineer

Summary of Role

The Senior Analytics Engineer leads the design of the silver and gold layers and sets the modelling, testing and deployment standards the chapter builds to. The role certifies the chapter's metrics and anchors the SAP BW to Databricks migration on the analytics side. The rest of the business reports on the gold layer this role keeps trustworthy.

Responsibilities

Architecture and Modelling Leadership

• Design the silver and gold layer structure with Lakeflow Declarative Pipelines and dimensional modelling so that the estate stays consistent as it grows.
• Build the hardest models personally so that complex business rules land correctly.

Metric Certification

• Own the metric certification approach across gold tables, Unity Catalog metric views and their agent metadata so that people and AI agents consume one certified definition per measure.
• Provide BI-ready views over metric views so that Power BI consumes certified measures without duplicating logic that already exists in gold.

Quality and Migration Assurance

• Own the reconciliation approach between Databricks and SAP BW during the migration so that sign-off is evidence-based.
• Set the testing standard of expectations and unit tests and hold the chapter to it.

Standards, Coaching and Delivery Governance

• Set and maintain modelling, review and deployment standards so that quality does not depend on individuals.
• Coach junior and intermediate engineers through reviews and pairing so that the chapter's capability grows.

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.
• 6 or more years in analytics engineering, data engineering or advanced analytics, with production ownership of models other teams depend on, and experience setting standards or reviewing other engineers' work.
• Expert SQL and dimensional modelling at scale.
• Production experience with a declarative transformation framework, including CDC and slowly changing dimensions: Lakeflow Declarative Pipelines or dbt.
• Production experience on Databricks itself, or a current Databricks Certified Data Engineer Professional certification.
• Git, CI/CD and deployment automation: pull requests, GitHub Actions, Databricks Asset Bundles or equivalent.
• Deep SAP knowledge: BW objects, and S/4HANA or Business Data Cloud, strong enough to lead source interpretation during the migration.
• Working Python: the unit-testing APIs and the chapter's pipeline framework are Python, and this role sets the testing standard.

Advantageous

• Unity Catalog: governance grants, masking, row filters and lineage, and semantic-layer curation through metric views.
• Coding agents such as Claude Code or Codex, run in parallel and verified by tests and reconciliation.
• Retail data, ideally pharmacy retail. Healthcare or financial services also count.
• A completed migration off a legacy warehouse.