Intermediate Data Engineer

SQL

CI/CD pipelines

Hadoop

SQL MS 

Data engineering

Data warehousing

Python, Java, or Scala 

Analytical

Machine Learning

Problem Solving

Data Platform Engineering:

• Build, maintain, and improve batch and near-real-time data pipelines that support analytics and operational use cases.


• Contribute to ingestion, transformation, storage, and serving layers within the enterprise data platform.


• Develop reliable and maintainable data processing logic using SQL, Python, and related tooling.


• Help modernise legacy data workflows into more scalable and supportable patterns.

Lakehouse Architecture & Scalable Processing:

• Contribute to the implementation and improvement of data lake or lakehouse components.


• Support the preparation of data for efficient analytical use across warehouse and large-scale storage environments.


• Assist with data layout, partitioning, and optimisation practices that improve platform performance and maintainability.


• Work with senior engineers to apply platform standards and architectural patterns consistently.

Data Quality, Reliability & Governance:

• Build and maintain data quality checks, reconciliation logic, and pipeline validation routines.


• Help ensure datasets meet expectations for freshness, completeness, and accuracy.


• Support metadata, lineage, and documentation practices that improve trust and transparency.


• Troubleshoot pipeline failures, data discrepancies, and performance issues in production.

Collaboration & Delivery:

• Partner with BI, analytics, software engineering, product, and business stakeholders to understand and support data use cases.


• Translate business and platform requirements into scalable, well-engineered technical solutions.


• Participate in planning, estimation, implementation, testing, and deployment activities.


• Contribute to code reviews, documentation, and shared engineering standards.

Continuous Improvement & Innovation:

• Identify opportunities to improve automation, reliability, scalability, and maintainability across the platform.


• Stay current with emerging data engineering practices, tools, and patterns.


• Grow technical capability in modern data platform engineering through hands-on project delivery.

Tech Environment:

The platform may include a combination of established and modern technologies such as: SQL Server, Python, Spark, Flink, Airflow, Object storage, Open format Tables, Kafka / Redpanda, ClickHouse or similar columnar analytical stores, Git and CI/CD tooling, Kubernetes, OpenShift, or similar runtime environments.

• Degree or diploma in IT, Computer Science, Engineering, or a related technical discipline.


• 2 to 4 years of experience in data engineering, ETL/ELT development, or data platform engineering.


• Hands-on SQL experience, including query optimisation, indexing, and performance tuning.


• Experience building and maintaining batch data pipelines in production environments.


• Exposure to data platform concepts such as data warehouse, data lake architecture, or object storage.


• Experience with orchestration platforms such as Airflow, Dagster, or similar.

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.