| Data Management | Devise & implement MDM processes (classification, security, quality, ethics, retrieval & retention) | 4 | 5 | Introduced soft/hard delete strategy; implemented RPV & last-seen frameworks; designed employee / project / customer domain MDM structures; strong data security awareness (container-level). | M |
| Derive metadata structures to support consistent retrieval & interpretation | 5 | 5 | Created CDM meta layers (codes, dates, values); JSON metadata pattern; lineage & dictionary automation in progress. | L |
| Plan effective data storage, sharing & publishing | 4 | 5 | Established medallion architecture (Bronze → Silver → Gold → Platinum); governed use of Fabric, Synapse & ADF; rationalised SQL and Storage Accounts to reduce cost. | M |
| Independently validate external information from multiple sources | 3 | 4 | Demonstrated with IFS vs ERP feeds, CSV vs API validation; partial automation but scope to strengthen systematic validation across APIs. | H |
| Assess issues preventing optimal use of information assets | 5 | 5 | Recognised governance, API, and structural blockers; led pragmatic discussions with DBAs; positive agile approach to resolving data access friction. | L |
| Data Modelling & Design | Set standards for data-modelling tools, techniques & compliance | 4 | 5 | Developed consistent CDM view patterns (core/meta/item); naming & schema conventions; metadata-driven SQL generation. | M |
| Manage investigation of corporate data requirements | 4 | 5 | Established cross-domain analysis (Project, Employee, BusOpp, Invoice, Order); translated business needs into model structure; aligned with CRM/ERP. | M |
| Manage iteration, review & maintenance of data models | 5 | 5 | Iterative versioning of CDM and DataMart views; implemented patch/put/post framework for updates; continuous refinement evidenced. | L |
| Database Design | Guide database / warehouse architecture selection | 4 | 5 | Led architectural choices between Synapse vs Fabric; created dynamic table creation scripts; cost-aware performance design. | M |
| Provide specialist expertise in DBMS / warehouse design characteristics | 4 | 5 | Demonstrated deep understanding of partitioning, Fabric constraints, metadata-driven design; provides guidance to others. | M |
| Ensure physical database design supports transactional data performance | 3 | 4 | Effective with Data Factory + Lakehouse ingestion; could expand formal testing and performance baselining. | H |
| Ensure warehouse design supports BI / analytics demands | 5 | 5 | Designed for Power BI and unified currency reporting; maintains business-ready marts. | L |
