Senior Analytics Engineer

MOO | Cape Town, Western Cape, ZA | on_site

Apply for the Senior Analytics Engineer role at MOO in Cape Town, South Africa. Permanent full-time hybrid opportunity using SQL, dbt, Snowflake, Tableau and governed semantic-layer analytics.

Job description

MOO is hiring a Senior Analytics Engineer for its Business Intelligence team in Cape Town, South Africa. The role owns analytical domains end to end: partnering with business stakeholders, defining trusted metrics in a governed semantic layer, modelling data in dbt, and ensuring KPIs remain consistent across dashboards, self-service tools and AI-assisted analytics. MOO's modern analytics stack includes Snowflake, dbt and Dagster, with Tableau currently used for business intelligence reporting.

Responsibilities

  • Partner with stakeholders across operations, commercial, finance and supply chain to design data models and unlock analytical capabilities.
  • Define metrics, dimensions and business logic in the semantic layer using dbt Semantic Layer and/or Snowflake semantic views.
  • Develop and maintain production data models in dbt and help ensure trusted KPI definitions across the organisation.
  • Deliver reporting through BI tooling, currently Tableau, while reducing duplicated or conflicting analytical outputs.
  • Curate and validate governed datasets and semantic models for AI and natural-language consumption.
  • Act as a quality and accuracy bar for AI-generated analysis based on governed business data.
  • Promote healthy self-service analytics and stronger data literacy across business teams.
  • Review other engineers' work constructively and contribute to modelling standards.
  • Demonstrate new capabilities and train business stakeholders when required.

Requirements

  • Strong SQL skills and production experience with dbt.
  • Production experience on a cloud data warehouse using Git-based, review-first engineering workflows.
  • A track record of defining business metrics with stakeholders and delivering analytical outcomes people rely on.
  • Strong judgement about where business logic should live, such as the semantic layer versus BI/reporting layers.
  • Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
  • Strong business acumen and the ability to challenge metric definitions so they reflect real business outcomes.
  • Practical interest in how analytics is evolving alongside AI, with a rigorous approach to validation.

Skills

  • SQL
  • dbt
  • Snowflake
  • Data Modelling
  • Analytics Engineering
  • Semantic Layer
  • Tableau
  • Dagster
  • Cloud Data Warehousing
  • Git
  • Business Intelligence
  • Data Governance
  • Metric Definition
  • Stakeholder Management
  • AI-Ready Data
  • Natural-Language Analytics
  • Data Quality
  • Self-Service Analytics