About this role
- Proficiency in SQL and data querying fundamentals.
- Understanding of relational databases and structured data management.
- Knowledge of data pipelines, ETL/ELT processes, and batch data processing concepts.
- Ability to troubleshoot, debug, and resolve technical issues.
- Familiarity with version control systems, particularly Git.
- Exposure to Python or similar scripting languages for data-related tasks.
- Understanding of data warehousing, dimensional modelling, and analytical data concepts.
- Familiarity with workflow orchestration tools such as Airflow or SQL Server Agent.
- Knowledge of cloud and on-premises storage concepts.
- Awareness of distributed processing, streaming architectures, and lakehouse technologies.
- Exposure to modern data engineering tools and platforms, including Spark, PySpark, Kafka, Redpanda, ClickHouse, Iceberg, and Delta Lake.
- Understanding of monitoring, observability, alerting, and operational support practices for data workloads.
- Familiarity with CI/CD pipelines, deployment automation, and engineering best practices.
- Ability to work effectively with technical documentation, runbooks, and team standards.
- Strong analytical, problem-solving, and communication skills.
- Ability to collaborate with technical and non-technical stakeholders.
- High attention to detail when working with business-critical data.
- Strong accountability, ownership mindset, and commitment to continuous learning and professional growth in data engineering.
Job Specification:
- Degree, diploma, or relevant certification in IT, Computer Science, Engineering, Information Systems, or a related technical discipline.
- Minimum 1+ years proven experience in data engineering, software development, ETL/ELT, database development, or a related technical role.
- Foundational SQL skills, including writing queries, joining datasets, and working with basic transformations.
- Some exposure to Python, data processing, scripting, or automation.
- Understanding of relational databases and structured data concepts.
- Willingness to learn modern data platform tools, engineering practices, and production support processes.
Technical Skills
You have a basic working understanding of:
- SQL and data querying fundamentals.
- Relational databases and structured data handling.
- Data pipelines, ETL/ELT, or batch-processing concepts.
- Debugging and troubleshooting technical issues.
- Version control concepts such as Git.
Exposure to the following would be advantageous:
- Python or similar scripting languages.
- Data warehousing, dimensional modelling, or analytical data concepts.
- Workflow orchestration tools such as Airflow or SQL Server Agent.
- Cloud or on-premises object storage concepts.
- Distributed processing, streaming, or lakehouse concepts.
- Tools such as Spark, PySpark, Kafka, Redpanda, ClickHouse, Iceberg, or Delta Lake.
Platform & Engineering Practices:
- Interest in learning monitoring, alerting, observability, and operational support for data workloads.
- Exposure to CI/CD, deployment automation, or engineering workflows is beneficial.
- Comfort working with documentation, runbooks, and team standards.
Personal Attributes:
- Positive learning mindset and willingness to ask questions.
- Strong sense of accountability and follow-through.
- Good problem-solving and communication skills.
- Comfortable collaborating with both technical and non-technical stakeholders.
- Attention to detail and willingness to work carefully with business-critical data.
- Motivation to build a long-term career in data engineering.
Living the Spirit
- Engages in cross-functional collaboration and problem solving while contributing to an inclusive team culture.
- Supports a culture of adaptability and shared accountability across the department and wider business.
- Shows up authentically and contributes to team success by working effectively with diverse colleagues and perspectives.
- Approaches challenges as opportunities to learn, improve, and help others grow.