SFIA Reference: INCA Level 4/5 Data Engineer (Grade 3) Competency Description Develops secure, efficient, and responsive integration methods for streaming or API-driven data. Learning Outcomes Evidence Requirements Level 4: Level 5: Suggested Learning Activities Recommended Resources
SFIA Reference: DBDS Level 4/5 Data Engineer (Grade 3) Competency Description Designs optimised, scalable, and governed data models to support analytics and storage efficiency. Learning Outcomes Evidence Requirements Level 4: Level 5: Suggested Learning Activities Recommended Resources
SFIA Reference: DENG Level 4/5 Data Engineer (Grade 3) Competency Description Designs, develops, and maintains data pipelines, ensuring scalability, performance, and quality in support of enterprise reporting and analytics. Learning Outcomes Evidence Requirements Level 4: Level 5: Suggested Learning Activities Recommended Resources
Role: Project ManagerContext: Large-scale technical consultancy engagement requiring coordination across engineering, finance, and delivery teams 🔪 Challenge The project manager needed to: Problems included: “I spent hours each week trying to make sense of data from five different places—and still couldn’t guarantee it was accurate.” 🚀 Solution The Data Engineering team: 🌟 Outcome “Now I … Read more
SFIA-8 Aligned Overview As a Senior Data Engineer (Grade 4) you design, build, and optimise scalable data platforms, pipelines, and architectures. In this role, you lead data engineering best practices, DevOps, automation, and real-time processing, working closely with Data Architects, Analysts, and Data Scientists to drive business value from data. You play a key role … Read more
(Revised for SFIA-8 Alignment) Overview As a Data Engineer (Grade 3) you support the development of scalable, efficient, and secure data solutions. You work closely with Data Architects, Analysts, and Business Teams to ensure the effective management of structured and unstructured data across our enterprise. As part of the Data Engineering team, you are responsible … Read more
Microsoft Fabric is designed to integrate multiple analytics workloads, including data engineering, data science, and business intelligence, within a unified platform. While our current Medallion structure already has a logical progression of data refinement (Bronze → Silver → Gold), there are key differences in architecture, tooling, and approach that we might consider while emulating a … Read more
1. Base DataViews & DataMart Architecture 2. CRM and Transactional Data 3. Data Security and Governance 4. Signposting of Insights 5. Metadata and Data Lineage 6. Master Data Management (MDM) 7. ETL/ELT and Data Integration Processes 8. Analytics and Data Modelling 9. Data Lifecycle Management & Archiving 10. Data Quality and Stewardship 11. Real-Time Data … Read more
Objective: Design, develop, deploy, and maintain Datamarts with a focus on performance, security, and usability. Steps: Data Discovery and Requirements Gathering Datamart Design and Security Planning MVP Development and Iteration Deployment and Go-Live Maintenance and Performance Testing
Objective: Efficiently acquire data from various sources and ensure secure, accurate, and timely transport to the data lakehouse and data warehouse. Steps: Data Source Identification and Mapping Data Profiling and Classification Data Acquisition and Integration Transport to Data Warehouse Testing and Validation