Middle Data Engineer

Responsibilities:

• Build, maintain, and optimise scalable ETL/ELT pipelines (batch and near-real-time) on Azure Data Cloud Platform (e.g., Data Lake, Microsoft Fabric, Azure Data Factory).
• Develop and refine data models to support BI reporting, analytics, and ML/AI use cases.
• Write efficient, well-documented T-SQL and PySpark code following team coding standards.
• Implement automated testing, data validation, and monitoring (SLAs, alerts) to ensure pipeline reliability.
• Contribute to data governance practices, including lineage tracking, metadata management, and quality controls.
• Support CI/CD pipelines for data assets, ensuring version control and reproducibility.
• Partner with analytics engineers to scope, refine, and prioritise data requirements from business stakeholders.
• Work with Analysts, BI Developers, Data Scientists, and business teams to translate requirements into production-ready data solutions.
• Provide input on data readiness for machine learning and analytics projects.
• Contribute to the evolution of the ED&I data platform, including tooling, standards, and documentation.
• Stay current with emerging data engineering patterns and technologies; propose improvements to team processes.
• Leverage AI-driven development tools (e.g., generative-AI code assistants, automated data profiling) to accelerate delivery.
• Support performance tuning and cost optimisation across the data platform.

Requirements:

• 3+ years in data engineering or a closely related role.
• Bachelor’s degree in Computer Science, Data Engineering, or a related field.
• Strong T-SQL skills and working proficiency in PySpark or Python for data processing.
• Hands-on experience with MS Azure Storage Explorer and SSMS.
• Hands-on experience with cloud-based data engineering services and orchestration tools (e.g., Azure Data Factory, Microsoft Fabric).
• Practical experience building ETL/ELT pipelines and dimensional or analytical data models.
• Familiarity with CI/CD practices in data engineering, including version control (Git) and automated testing.

Nice to have:

• Experience with real-time or streaming data architectures.
• Experience with PowerShell, Apache Kafka, and/or KQL.
• Exposure to AI/ML workflows (feature engineering, data preparation for model training).
• Familiarity with Power BI or other BI/visualisation tools.
• Experience using AI productivity tools (e.g., ChatGPT, Claude, Copilot, Cursor) in day-to-day and data engineering tasks.
• Understanding of data security, privacy, and compliance considerations.