Senior Data Engineer
Modern Data Engineer (Enterprise Data Platforms)
Role Summary
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.
Minimum Technical Requirements
Candidates must demonstrate practical project experience with:
Software Engineering
- Python as a primary programming language
- Software engineering principles and clean coding practices
- Object-oriented programming
- Testing and code quality practices
- Software Development Life Cycle (SDLC)
- Git and collaborative development workflows
- API development and integration
- CI/CD pipelines
- Production software deployment
Engineering Mindset
Candidates should demonstrate the ability to:
- Solve business problems through code
- Design scalable solutions
- Work within engineering teams
- Contribute to production systems
- Follow engineering standards and best practices
Core Data Engineering Requirements
Candidates should have hands-on experience with several of the following:
Data Processing
- Spark
- PySpark
- Databricks
- Data Lake architectures
- Batch processing
- Streaming architectures
- Data transformation frameworks
- ETL/ELT design
Data Platform Technologies
- Kafka
- Flink
- Delta Lake
- Iceberg
- Airflow
- Modern orchestration platforms
Data Storage & Analytics
- SQL
- Data modelling
- Data warehousing concepts
- Relational databases
- Analytical data platforms
Cloud Engineering Requirements
Candidates should have practical experience delivering solutions on at least one major cloud platform:
Preferred Order
- Google Cloud Platform (GCP)
- Amazon Web Services (AWS)
- Microsoft Azure
Typical Technologies
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 should reflect real project delivery rather than certifications alone.
DevOps & Platform Engineering
Strong candidates should also demonstrate exposure to:
Containerisation & Deployment
- Docker
- Kubernetes
Infrastructure
- Terraform
- Infrastructure as Code
- Environment management
Operations
- Monitoring
- Observability
- Logging
- Production support
Data Modelling Requirements
Candidates should possess working knowledge of:
Data Architecture
- Conceptual modelling
- Logical modelling
- Physical modelling
Data Warehousing
- Dimensional modelling
- Relational modelling
- Data warehouse design
- Data governance concepts
Data modelling should support modern platform development, not exist in isolation.