Analytics Engineer II

Techrepo.co.za; Stellenbosch; On-site; ZAR 900,000 - 1,200,000; Full time

Job summary

Techrepo.co.za is seeking a technically versatile Analytics Engineer to bridge data engineering and data analytics, transforming complex data into reliable, scalable models and insights. You will collaborate with architects and modellers to design enterprise data strategies and deliver efficient data assets and pipelines that empower stakeholders.

You will work with modernised environments, maintain data quality, productionise reports, and apply CI/CD practices, with a focus on high-volume data

Qualifications

• Honours or Bachelor's degree in analytics/engineering or STEM with 5+ or 8+ years of relevant experience
• Experience delivering scalable data solutions and leading projects
• Experience with high-volume data environments and data governance practices
• Proficiency with CI/CD, testing frameworks and orchestration tools (Airflow/Prefect)

Responsibilities

• Build scalable data models, marts and semantic layers
• Design reproducible transformations and pipelines across environments
• Collaborate with Data Architects and Data Modellers on enterprise data strategy
• Maintain data quality and governance during migration
• Productionise reports using engineering best practices
• Work with CI/CD pipelines and orchestration tools like Airflow
• Leverage AI tools as productive partners in data work
• Deliver insights with speed, consistency and minimal friction

Skills

Analytics engineering

Data modelling

Data governance

High-volume data

Education

Honours Degree in Analytical/Data/Technical or Engineering - Other

Bachelor's Degree in Analytical/Data/Technical or Related Field

Equivalent experience/certifications

Tools

Airflow

CI/CD

Prefect

AI tooling (Copilot, Claude)

Job description

Job Description

About the Role

We're looking for a technically versatile Analytics Engineer to sit at the intersection of data engineering and data analysis - To bridge the gap between Data Engineers and Data Analysts by transforming complex data into accessible, reliable and performant data models and insights that drive decision‑making across the organisation. You'll collaborate with stakeholders to understand analytical needs, partner with Data Architects and Data Modellers to design scalable models, and build the efficient, reproducible data assets - including data models and marts, semantic layers and working environments - along with the transformations and pipelines that let analysts deliver insights with speed, consistency and minimal frictionat a time when both worlds are running side by side.

Responsibilities

• Build scalable data models, marts and semantic layers
• Design reproducible transformations and pipelines across legacy and modernised environments
• Partner with Data Architects and Data Modellers to align on enterprise data strategy
• Maintain data quality and governance standards throughout the migration
• Productionise reports using engineering principles
• Work with CI/CD pipelines, testing frameworks and orchestration tools like Airflow3
• Use AI tools - including Copilot and Claude - as active partners in your work
• Honours degree in an analytics/engineering or STEM or related field, PLUS at least 5yrs relevant experience OR
• Bachelor's degree in an analytics or STEM or related field, PLUS at least 8yrs relevant experience OR
• Equivalent experience of 8 yrs relevant experience and/or relevant certifications.
• At least 5 years' experience in analytics engineering or a related role
• Proven track record of delivering scalable data solutions and leading projects
• Experience with high-volume data environments
• Experience with CI/CD, testing frameworks and orchestration tools (e.g., Airflow)
• Experience productionising reports using engineering principles

Requirements

• Bachelor's Degree in Analytical/Data/Technical or Other
• Clear criminal and credit record

Preferred Qualifications

• Honors Degree in Analytical/Data/Technical or Engineering - Other
• Advanced grasp of:
• Data modelling best practices.
• Data governance and quality assurance.
• Cloud data platforms and orchestration tools (e.g., Airflow, Prefect).
• Understanding of software engineering principles (e.g., CI/CD, testing).