Junior Data Engineer

Minimum Education (essential)

• Bachelor's degree in Computer Science, Information Systems, Data Engineering or a related field.
• Relevant Microsoft Fabric and/or Azure data certification.

• 0-1 years of practical data engineering experience, including strong recent hands-on experience with Microsoft Fabric.

• Strong hands-on experience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Pipelines/Data Factory, notebooks and Dataflows Gen2.
• Strong SQL Server and T-SQL capability, including complex query development, schema design, indexing and performance optimisation.
• Practical experience developing, maintaining and supporting production ETL/ELT pipelines.
• Experience integrating and extracting data from REST/SOAP APIs, databases, flat files and other structured or unstructured data sources.
• Proficiency in data transformation using SQL and Python/PySpark, with an understanding of scalable data processing practices.
• Practical experience in data warehousing, dimensional modelling, incremental loading, orchestration and schema evolution.
• Experience with troubleshooting pipeline failures, data quality issues and performance bottlenecks, including the ability to restore service efficiently.
• Experience with source control, CI/CD and deployment practices using Git, Azure DevOps or equivalent tools.
• Experience supporting Power BI and other downstream analytical or reporting requirements.
• Demonstrated ability to take ownership of an existing technical environment with limited hand-holding.
• Strong documentation, communication and stakeholder engagement skills.

• Microsoft Fabric: OneLake, Lakehouse, Warehouse, Data Pipelines/Data Factory, notebooks and Dataflows Gen2.
• SQL Server / T-SQL
• Python / PySpark
• REST/SOAP APIs and structured/unstructured data ingestion.
• ETL/ELT, incremental loading, orchestration and scheduling.
• Dimensional modelling, medallion architecture, schema evolution and data warehousing.
• Power BI integration and understanding of downstream analytical requirements.
• Git / Azure DevOps, CI/CD and environment deployment practices.
• Monitoring, data quality, performance optimisation, security and operational support.

• Proficient in Afrikaans and English.
• Own transport and valid driver’s license.

KEY PERFORMANCE AREAS AND OBJECTIVES

Fabric Data Engineering and Pipeline Development

• Take ownership of the existing Microsoft Fabric data engineering environment and become productive quickly following handover.
• Design, develop, maintain and orchestrate reliable batch and near-real-time data ingestion pipelines using appropriate Microsoft Fabric capabilities.
• Extract and ingest data from structured and unstructured sources, including REST APIs, SOAP APIs, databases and flat files.
• Develop robust data transformation logic using SQL, Python/PySpark, Fabric notebooks and Dataflows Gen2, as appropriate.
• Implement incremental loading, retry mechanisms, logging, monitoring and alerting to support data integrity and pipeline reliability.
• Troubleshoot and resolve pipeline failures and data processing issues efficiently.
• Optimise data pipelines and processing workloads for performance, scalability and cost-effectiveness.

• Design, manage and evolve scalable data architectures using Microsoft Fabric, OneLake, Lakehouse, Warehouse and SQL Server.
• Maintain appropriate data-layering and medallion architecture principles, where applicable, with clear movement from raw to curated data.
• Develop and maintain robust schema designs, indexes, partitioning and query strategies to support analytical and operational workloads.
• Manage schema evolution and version control to maintain consistency and minimise disruption to downstream consumers.
• Maintain metadata, data dictionaries, architecture documentation and technical documentation to improve supportability and reduce key-person dependency.
• Define and maintain appropriate role-based access and security controls.

• Build and maintain analytical data stores using Microsoft Fabric Warehouse and/or Lakehouse patterns.
• Apply appropriate data-loading, partitioning, storage optimisation and query-performance practices.
• Develop and maintain stable, well-modelled datasets for Power BI and other analytical consumers.
• Work with reporting and analytical teams to investigate and resolve data-related issues.
• Ensure data structures and outputs support downstream reporting and business intelligence requirements.

• Develop and maintain conceptual, logical and physical data models.
• Apply dimensional modelling techniques, including star and snowflake schemas, to support analytics and reporting.
• Apply appropriate normalisation and relational modelling techniques for operational and analytical workloads.
• Ensure consistency of data models across systems.
• Manage schema versioning and evolution without unnecessarily disrupting downstream consumers.
• Apply agreed data engineering standards and modelling principles consistently.

• Work independently and take end-to-end ownership of assigned data engineering deliverables, incidents and production issues.
• Provide clear and timely updates regarding progress, risks, dependencies and blockers.
• Engage directly with technical and business stakeholders to clarify requirements and agree practical solutions.
• Explain technical concepts and trade-offs in a manner appropriate to the relevant stakeholder.
• Maintain practical technical documentation, including runbooks, architecture notes, change logs and release notes.
• Take accountability for the successful delivery and operational support of assigned solutions.

• Automate recurring data engineering and operational activities where practical.
• Implement monitoring and alerting to identify data quality issues, pipeline failures and abnormal processing behaviour.
• Analyse and optimise query, notebook and pipeline performance across SQL Server and Microsoft Fabric.
• Monitor capacity and resource utilisation and contribute to scalability and cost-control decisions.
• Deploy solutions using appropriate CI/CD and controlled deployment practices.

• Apply data security best practices, including secure authentication, least-privilege access and appropriate encryption.
• Ensure data engineering solutions comply with applicable data governance policies and regulatory requirements.
• Apply sound engineering practices relating to recoverability, auditability, supportability and controlled change.
• Protect confidential and sensitive business information.

• Collaborate with developers, data analysts, data scientists and business stakeholders to understand requirements and deliver practical solutions.
• Support effective handover and knowledge transfer to reduce key-person dependency within the data environment.
• Share technical knowledge and contribute to continuous improvement of team practices and the data environment.
• Provide guidance and support to junior team members where required.
• Remain accountable for the quality, reliability and timeliness of own deliverables.

• Document data processes, transformations, dependencies and architectural decisions.
• Validate data outputs through reconciliation, data quality checks and appropriate testing before production deployment.
• Maintain high standards of engineering quality by following agreed development, code review, testing, deployment, backup and archival practices.
• Ensure changes are appropriately tested, documented and controlled before implementation.
• Safeguard confidential information and data.
• Support compliance with applicable organisational policies, standards and regulatory requirements.

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