About this role
*About Woodland Mills*
Woodland Mills is a global manufacturer of innovative forestry equipment, committed to making high-quality solutions accessible to property owners everywhere. We combine engineering expertise with a customer-focused approach, delivering dependable products, and dedicated support. Our team values authenticity, resourcefulness, and continuous improvement, striving to create a straightforward and rewarding experience for every customer.
*Role Mandate*
Build and govern a trusted enterprise data foundation that turns operational data from ERP, CRM, ecommerce, service, logistics and other systems into secure, reusable data products.
*Position overview*
The Data Analytics Architect designs, develops and supports the organization's modern analytics platform. The role establishes reliable data flows from operational systems into Microsoft Fabric, organizes data through governed lakehouse and warehouse patterns, and delivers trusted semantic models and Power BI reporting experiences. Working across business and technical teams, the Architect translates reporting and analytical needs into scalable data products while improving data quality, lineage, security, performance and adoption. This role suits a practical builder who can move between architecture, engineering, analysis and stakeholder conversations.
*Key responsibilities*
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.
·