Junior Data & AI Engineer

As a Junior Data and AI Engineer, you will build and maintain data pipelines that power real business decisions across ZURU’s global operations. Working with modern tools like Databricks, dbt, Fivetran, and AWS, you’ll gain end-to-end exposure to data systems while contributing to scalable, production-grade infrastructure. This is a hands-on role with real ownership from day one.

 

This role plays a key part in enabling data-driven decision-making across ZURU’s global business. By building reliable pipelines, improving data quality, and supporting platform scalability, you directly impact commercial, marketing, and logistics performance. Your work ensures teams can trust and act on data, while contributing to the long-term evolution of ZURU’s data platform and AI capabilities.

 

Roles & Responsibilities:

• Build and maintain data pipelines ingesting data from SAP, retail POS systems, logistics platforms, and marketing channels
• Migrate legacy pipelines into scalable, maintainable modern data architectures
• Monitor and improve data quality, identifying issues before they impact the business
• Support and enhance data platform infrastructure and tooling
• Collaborate with cross-functional teams to deliver reliable datasets and insights
• Contribute to end-to-end data workflows including ingestion, transformation, and governance
• Document processes, pipelines, and learnings clearly
• Continuously improve workflows using automation and AI tools

What You Bring To ZURU

Skills & Experience:

• Strong SQL skills (joins, window functions, CTEs)
• Experience using Python for scripts, pipelines, or automation
• Understanding of data pipelines and common failure points
• Ability to learn quickly, solve problems, and work with ambiguity
• Strong communication skills, including asking questions and documenting solutions

Bonus:

• Experience with dbt, Spark, or cloud data platforms (Databricks, Snowflake, BigQuery, Redshift)
• Exposure to AWS or similar cloud environments
• End-to-end data project experience (academic, personal, or professional)

As a Junior Data and AI Engineer, you will build and maintain data pipelines that power real business decisions across ZURU’s global operations. Working with modern tools like Databricks, dbt, Fivetran, and AWS, you’ll gain end-to-end exposure to data systems while contributing to scalable, production-grade infrastructure. This is a hands-on role with real ownership from day one.

 

This role plays a key part in enabling data-driven decision-making across ZURU’s global business. By building reliable pipelines, improving data quality, and supporting platform scalability, you directly impact commercial, marketing, and logistics performance. Your work ensures teams can trust and act on data, while contributing to the long-term evolution of ZURU’s data platform and AI capabilities.

 

Roles & Responsibilities:

• Build and maintain data pipelines ingesting data from SAP, retail POS systems, logistics platforms, and marketing channels
• Migrate legacy pipelines into scalable, maintainable modern data architectures
• Monitor and improve data quality, identifying issues before they impact the business
• Support and enhance data platform infrastructure and tooling
• Collaborate with cross-functional teams to deliver reliable datasets and insights
• Contribute to end-to-end data workflows including ingestion, transformation, and governance
• Document processes, pipelines, and learnings clearly
• Continuously improve workflows using automation and AI tools

What You Bring To ZURU

Skills & Experience:

• Strong SQL skills (joins, window functions, CTEs)
• Experience using Python for scripts, pipelines, or automation
• Understanding of data pipelines and common failure points
• Ability to learn quickly, solve problems, and work with ambiguity
• Strong communication skills, including asking questions and documenting solutions

Bonus:

• Experience with dbt, Spark, or cloud data platforms (Databricks, Snowflake, BigQuery, Redshift)
• Exposure to AWS or similar cloud environments
• End-to-end data project experience (academic, personal, or professional)