Job Description
We are looking for a Data Engineer who takes pride in building data infrastructure that actually works — reliably, at scale, and in production. This is a hands-on engineering role where you will design and deliver the pipelines, data models, and workflows that power analytics, AI, and business decision-making across the organisation.
If you care about data quality, clean architecture, and engineering that stands up under pressure, this role is for you.
What You'll Be Doing
Design, build, and maintain scalable data pipelines and data stores supporting analytics, reporting, and operational use cases
Develop and optimise ingestion, transformation, and orchestration workflows across structured and unstructured data sources
Embed data quality, lineage, reliability, and security controls throughout the engineering lifecycle
Collaborate with analysts, data scientists, and business stakeholders to translate requirements into robust data models and reusable datasets
Monitor and troubleshoot data jobs, improve pipeline performance, and support production operations and incident resolution
Contribute to technical documentation, coding standards, and deployment and testing automation
What We're Looking For:
Strong proficiency in SQL and data modelling — you can design and optimize schemas for performance and scalability
Hands-on experience building ETL/ELT pipelines — from ingestion through transformation to delivery
Solid Python skills and practical experience with cloud data platforms (AWS, Azure, or GCP)
Minimum 2 years of experience in a data engineering or closely related role
Bachelor's Degree in Computer Science, Information Systems, Data Science, or a relevant field
Good to have:
Experience with Spark or distributed data processing frameworks
Familiarity with data warehousing and data lake concepts and architectures
Understanding of data governance, security practices, and CI/CD pipelines for data workflows
