Data Platform Engineer

Data Platform Engineer

As a Data Platform Engineer, you will be responsible for designing, building, and evolving scalable data platforms and pipelines that enable enterprise applications, analytics, and AI-driven products. 

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines, ingestion frameworks, and transformation processes.
• Build and optimise data models to support analytics, reporting, operational systems, and AI use cases.
• Develop robust ETL/ELT solutions to enable efficient and reliable data movement across enterprise platforms.
• Support integration with enterprise data architectures and evolving cloud-based data ecosystems.
• Ensure data solutions align with governance, security, compliance, and enterprise architecture standards.

• Build automated data pipelines to support scheduled and real-time production workloads.
• Deliver production-ready data services, APIs, and reusable data assets for business and engineering teams.
• Implement data quality, validation, and integrity controls through testing, monitoring, and automation.
• Support migration and optimisation of legacy data processes into scalable, enterprise-grade solutions.
• Embed quality assurance practices throughout the data engineering lifecycle, ensuring all deliverables meet Definition of Done standards.
• Contribute to data observability, troubleshooting, incident resolution, and continuous platform improvement.

• Partner with Platform, Full Stack, AI, and business teams to deliver integrated data capabilities.
• Support Agile and sprint-based delivery models, contributing to iterative product development.
• Promote reusable data engineering patterns and engineering best practices across projects.
• Maintain technical documentation and contribute to data governance and operational processes.

Required Skills & Experience:

• Proven experience designing, building, and maintaining enterprise data platforms and data pipelines.
• Strong expertise in ETL/ELT processes, data integration, and data modelling techniques.
• Advanced SQL and Python programming skills.
• Experience with Azure data services, including Azure SQL, Blob Storage, Microsoft Fabric, or equivalent cloud data technologies.
• Experience working with modern data processing frameworks and scalable data architectures.
• Strong understanding of data governance, validation, versioning, and quality management principles.
• Experience delivering production-ready, scalable data solutions.
• Familiarity with APIs and data services integration.
• Knowledge of monitoring, observability, and operational support for data platforms.

Professional Skills:

• Strong analytical and problem-solving capabilities.
• Experience working within Agile delivery frameworks and sprint-based environments.
• Ability to collaborate effectively across engineering, AI, platform, and business teams.
• Excellent communication and technical documentation skills.
• Commitment to continuous learning and adoption of emerging data and AI technologies.