Data Engineer (Public Sector)

• 1-year contract, renewable
• Government project
• Hybrid work arrangement

We are seeking a Data Engineer to design, develop, and implement enterprise data models that support organisational analytics, operational reporting, and data sharing initiatives.

The successful candidate will work closely with business stakeholders and technical teams to understand the existing data landscape, design future-state data models, and implement data pipelines and transformations to operationalise these models.

Candidates should have strong experience in data modelling and data management practices, and the ability to translate complex data structures into clear, user-centric and technical representations to facilitate stakeholder discussions and system implementation. Strong experience in data modelling and translating business concepts into structured data models is a key requirement for this role.

Key Responsibilities:

Data Modelling & Architecture

• Analyse the current organisational data landscape and identify opportunities to improve data structures and integration.
• Design and document conceptual, logical, and physical data models to support enterprise data architecture.
• Develop data mapping and transformation logic between source systems and target data models.
• Translate complex data structures into clear visual diagrams and technical documentation to support stakeholder discussions and system implementation.
• Ensure data models follow best practices for scalability, performance, and maintainability.

Data Management & Governance

• Apply and adhere to the organisation's Master Data Management (MDM) and metadata management practices in the design and implementation of data models and pipelines.
• Ensure datasets are documented with appropriate metadata, definitions, and lineage to support discoverability and usability across the organisation.
• Collaborate with internal data governance teams and stakeholders to ensure data standards and naming conventions are consistently applied.
• Assist in refining and improving existing data management practices by providing feedback based on implementation experience.

Data Engineering & Implementation

• Implement data pipelines and transformation workflows to operationalise designed data models.
• Perform data analysis and profiling to understand source data structures and quality.
• Develop data ingestion, transformation, and preparation processes using in-house enterprise data platforms.
• Support the review, preparation, and publication of datasets on the organisation's data access portal.

Stakeholder Engagement

• Engage business stakeholders to refine and validate data models.
• Facilitate discussions with domain experts to ensure data models accurately represent business concepts.
• Produce user-friendly visual artefacts and documentation to support cross-functional understanding.

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Data Science, Software Engineering, or a related technical discipline.
• Candidates with strong relevant industry experience in data engineering or data modelling may be considered in lieu of formal qualifications.
• 2–5 years of experience in data engineering, data modelling, or data architecture.
• Experience designing data models and implementing data transformations.
• Proven experience leading enterprise data modelling initiatives.
• Experience defining data management frameworks or standards.
• Ability to mentor junior engineers and guide data modelling discussions.
• Strong experience in data modelling (conceptual, logical, and physical models).
• Experience performing data mapping and transformation design between systems.
• Ability to analyse complex datasets and derive appropriate data structures.
• Strong documentation and visualisation skills for communicating technical concepts.
• Possess working experience in the following areas and technologies:
• SQL and relational databases (e.g., PostgreSQL)
• Data engineering or ETL/ELT platforms
• Python for data processing
• Data modelling tools and diagramming
• Data virtualisation platforms (e.g., Denodo) – preferred
• Data preparation or data science platforms (e.g., Dataiku) – preferred
• Knowledge in one or more of the following:
• Master Data Management (MDM)
• Metadata management
• Data governance practices
• Data modelling standards and best practices