We are seeking engineers who combine strong software engineering fundamentals with modern data engineering expertise. Candidates should be capable of designing, building, deploying and operating cloud-native data platforms while applying software engineering best practices throughout the delivery lifecycle.
Strong SQL and data warehousing experience remain important but should complement broader engineering capability rather than define the candidate's profile.
Candidates must demonstrate practical project experience with:
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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
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ETL
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AWS - native data services
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Data stores
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Workflow systems
Candidates should demonstrate the ability to:
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Solve business problems through code
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Design scalable solutions
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Work within engineering teams
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Contribute to production systems
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Follow engineering standards and best practices
Candidates should have hands-on experience with several of the following:
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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
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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
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SQL
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Data modelling
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Data warehousing concepts
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Relational databases
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Analytical data platforms
Candidates should have practical experience delivering solutions on at least one major cloud platform:
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Google Cloud Platform (GCP)
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Amazon Web Services (AWS)
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Microsoft Azure
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BigQuery
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Dataflow
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Dataproc
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Pub/Sub
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GKE
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Cloud Storage
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Glue
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EMR
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Redshift
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Kinesis
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EKS
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S3
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Data Factory
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Synapse
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Databricks
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Event Hubs
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AKS
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Azure Storage
Cloud experience should reflect real project delivery rather than certifications alone.
Strong candidates should also demonstrate exposure to:
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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
Candidates should possess working knowledge of:
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Conceptual modelling
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Logical modelling
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Physical 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
Data modelling should support modern platform development, not exist in isolation.
