Build modern data platforms. Engineer for production. Solve real business problems through code.
Are you a Data Engineer with a strong software-engineering foundation, comfortable building, deploying and operating modern cloud-native data platforms?
We are looking for a Modern Data Engineer whose experience goes beyond traditional reporting, business intelligence or analytics. This role is for someone who approaches data engineering as an engineering discipline — writing production-quality code, working collaboratively through the development lifecycle and building scalable solutions that operate reliably in production.
Strong SQL, data modelling and data-warehousing capability remain important, but they should complement a broader software-engineering and modern data-platform background rather than define it.
What you will bring
Minimum Technical Requirements – Non-Negotiable
You must be able to demonstrate practical project experience in:
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Python as a primary programming language
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Software-engineering principles and clean coding practices
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Object-oriented programming
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Testing and code-quality practices
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Software Development Life Cycle (SDLC)
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Git and collaborative development workflows
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API development and integration
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CI/CD pipelines
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Production software deployment
You should also be able to demonstrate an engineering mindset, including the ability to:
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Solve business problems through code
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Design scalable solutions
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Work effectively within engineering teams
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Contribute to production systems
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Follow engineering standards and best practices
Modern Data Engineering
Your experience should include hands-on exposure across several modern data-engineering technologies and approaches, such as:
Data processing
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Spark
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PySpark
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Databricks
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Data Lake architectures
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Batch processing
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Streaming architectures
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Data transformation frameworks
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ETL/ELT design
Data platforms
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Kafka
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Flink
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Delta Lake
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Iceberg
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Airflow
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Modern orchestration platforms
Data storage and analytics
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SQL
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Data modelling
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Data warehousing
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Relational databases
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Analytical data platforms
We are looking for breadth across modern data engineering, rather than a candidate who simply recognises the technology names.
Cloud Engineering – Must-Have
Practical project-delivery experience on at least 1 major cloud platform is required.
Preference will be given in the following order:
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Google Cloud Platform (GCP)
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Amazon Web Services (AWS)
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Microsoft Azure
Relevant technologies may include:
GCP - BigQuery | Dataflow | Dataproc | Pub/Sub | GKE | Cloud Storage
AWS - Glue | EMR | Redshift | Kinesis | EKS | S3
Azure - Data Factory | Synapse | Databricks | Event Hubs | AKS | Azure Storage
Cloud experience must reflect practical project delivery rather than certification alone.
DevOps & Platform Engineering
Strong candidates will also bring exposure to areas such as:
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Docker
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Kubernetes
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Terraform
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Infrastructure as Code
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Environment management
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Monitoring
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Observability
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Logging
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Production support
Data Modelling & Architecture
You should have working knowledge across areas such as:
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Conceptual, logical and physical data modelling
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Dimensional modelling
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Relational modelling
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Data warehouse design
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Data governance concepts
Importantly, your data-modelling experience should support modern platform development rather than exist in isolation.
The profile we are looking for
The strongest candidates will combine:
Strong foundations
SQL | Data modelling | Data warehousing | ETL/ELT
Modern engineering capability
Python-first development | Git-first collaboration | CI/CD | Cloud-native development | Distributed processing | Streaming architectures
Engineering maturity
Production deployment | Solution ownership | Contribution across the SDLC | Agile engineering-team experience
This role will suit you if...
You are comfortable moving between code, data, cloud and production environments.
You think beyond getting a pipeline to run and consider scalability, quality, maintainability and operational reliability.
And most importantly, you see yourself as an engineer who happens to work with data, rather than someone whose experience is primarily centred on reporting or analytics.
