Data Engineer

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