Data Specialist

1. Data architecture and platform design

• Design and maintain a practical enterprise data architecture using Microsoft Fabric, aligned to business priorities, source-system realities and security requirements.
• Define clear separation between raw, validated and curated data, using lakehouse and warehouse patterns appropriate to the structure, scale and consumption needs of each domain.
• Develop reusable data-domain models for areas such as customers, products, sales, inventory, purchasing, service, finance, logistics and digital engagement.
• Establish conventions for workspaces, naming, environments, storage, refresh, orchestration, ownership and lifecycle management.

2. Data integration and engineering

• Build and support ingestion and transformation pipelines from Dynamics 365 Business Central, Dynamics 365 Customer Engagement, Dataverse, ecommerce, Google Analytics, SharePoint, files, APIs and other operational sources.
• Create reliable transformation logic for cleansing, standardization, deduplication, conformance, historization and business-rule application.
• Implement incremental processing, scheduling, dependency handling, logging, alerting and recovery patterns that improve reliability and supportability.
• Partner with application owners to resolve source-data issues while preserving traceability between source records and analytical outputs.

3. Data warehousing and modelling

• Design dimensional and analytical models that provide consistent facts, dimensions, keys, hierarchies and business definitions.
• Create curated warehouse and lakehouse structures optimized for Power BI, ad hoc analysis and downstream data products.
• Maintain shared business calculations and measures so that key metrics are defined once and reused consistently.
• Balance usability, performance, maintainability and cost when selecting storage, transformation and serving patterns.

4. Power BI and semantic modelling

• Develop governed Power BI semantic models, reports and dashboards that answer operational and strategic questions.
• Use effective model relationships, DAX, calculation logic, row-level security and refresh design to deliver trusted analytics.
• Promote a thin-report and reusable-model approach where practical, reducing duplicated logic and inconsistent metrics.
• Coach business users on interpreting metrics, navigating reports and using certified or endorsed datasets for self-service analysis.

5. Data governance, quality and security

• Define and monitor data-quality controls, including completeness, validity, uniqueness, timeliness and reconciliation checks.
• Document lineage, transformations, business definitions, ownership, access rules and known limitations for key data products.
• Apply least-privilege access, appropriate workspace and item permissions, and secure handling of confidential or regulated data.
• Support retention, audit, privacy and compliance needs in collaboration with business, IT and leadership stakeholders.

6. Delivery, support and continuous improvement

• Gather and prioritize analytical requirements, convert them into deliverable data products, and communicate trade-offs clearly to technical and non-technical audiences.
• Use source control, peer review, testing and structured deployment practices to improve quality and reduce change risk.
• Monitor platform health, pipeline execution, refresh performance, capacity use and report adoption, then recommend targeted improvements.
• Create architecture diagrams, runbooks, data dictionaries, support documentation and user guidance that make the platform sustainable.