Data Pipeline development
• Design, build and maintain data pipelines for AI, analytics and automation solutions.
• Ingest, transform and deliver data from enterprise systems, files, databases and approved platforms.
• Build repeatable ETL/ELT processes that are reliable, scalable and maintainable.
• Monitor pipeline performance and resolve data flow issues where required.
Data Integration & Platform Support
• Integrate data across Microsoft enterprise systems, cloud platforms and business applications.
• Support data movement between source systems, data platforms, AI environments and reporting tools.
• Develop connectors, APIs or data services where required to support solution delivery.
• Work with existing enterprise platforms; Microsoft Fabric, Databricks, Power Platform and related cloud services.
• Implement monitoring, error handling, lineage, and auditability for data pipelines and datasets.
• Support data modelling for analytics, AI/ML and application use cases.
• Ensure datasets are accurate, consistent, documented and fit for intended use.
• Work with data owners and stakeholders to resolve data quality issues.
• Prepare and structure data for machine learning, model evaluation and AI solution development.
• Support feature engineering, training datasets, evaluation datasets and production data flows.
• Work with AI/ML Engineers to ensure models receive reliable and governed data inputs.
• Support ongoing data requirements for deployed AI and automation solutions.
Security, Governance & Documentation
• Apply access control, privacy, security and data governance requirements to data solutions.
• Document data flows, transformations, dependencies and known limitations.
• Maintain clear handover and support documentation for pipelines and integrations.
• Ensure data engineering work aligns with architectural and operational standards.