Data Engineer

Role Summary:

We are seeking a passionate and detail-oriented Data Engineer to join our Digital Tech & Data team. In this role, you will be responsible for building and maintaining the data pipelines that support our digital ecosystem — from ingesting raw data across multiple digital touchpoints to delivering clean, robust data that business teams can trust and act on.

You will bring engineering rigour to data — treating pipelines, models, and infrastructure as production-grade software — while staying closely connected to the digital business context your work enables.

Responsibilities:

• Design, build, and maintain production-grade data pipelines in Airflow that ingest data from digital touchpoints into Snowflake.
  
  

• Develop modular, tested, and well-documented dbt models that transform raw data into reliable, business-ready datasets — owning the full lifecycle from source definition to exposure.
  
  

• Provision and manage cloud data infrastructure (Snowflake objects, Airflow environments, supporting GCP resources) through Terraform, with everything version-controlled and peer-reviewed.
  
  

• Implement and uphold data quality, observability, and testing standards across pipelines
  
  

• Tune Snowflake performance and manage warehouse cost — clustering, query profiling, resource monitors and treat cost as a first-class engineering concern.
  
  

• Implement pipelines to platform standards — branching strategy, CI/CD for dbt and Airflow, code review norms, documentation, naming conventions
  
  

Required Qualifications:

Education:

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related discipline.
  
  

Experience:

• 2–3 years of hands-on experience in a data engineering or analytics engineering role, ideally within a digital or e-commerce environment.
  
  

Preferred Qualifications:

• Proven experience building and maintaining production-grade data pipelines using Apache Airflow.
  
  

• Strong working knowledge of Snowflake, including data modelling, performance tuning, clustering, and warehouse cost management.
  
  

• Demonstrated experience developing dbt projects — writing modular, tested, and well-documented transformation logic.
  
  

• Practical experience using Terraform to provision and manage cloud data infrastructure in a repeatable, version-controlled manner.
  
  

• Proficiency in Python for writing data pipelines, custom operators, and utility tooling.
  
  

• Strong SQL skills with the ability to write and optimise complex queries across large datasets.
  
  

• Fluency in driving an AI coding tool across pipeline codebase: scoping changes, providing the right files as context.