This role partners closely with business stakeholders, architects, and analytics teams to ensure high‑quality, reliable, and secure data pipelines from D365 and related enterprise systems
Roles and Responsibilities
- Demonstrate strong stakeholder management by partnering with business leaders, product owners, D365 functional consultants, and analytics teams to align data solutions with business priorities
- Design, develop, and maintain robust data pipelines and integrations sourcing data from Microsoft Dynamics 365 (Finance, Supply Chain, Sales, Customer Service, or CE) and related systems
- Build and optimize ETL/ELT processes using Azure-native services (e.g., Fabric).
- Model and transform data into analytics-ready structures (star/snowflake schemas) for reporting and BI.
- Implement data quality, validation, monitoring, and reconciliation processes for D365 data.
- Partner with functional consultants and business users to translate D365 business processes into scalable data models.
- Optimize performance, cost, and reliability of data pipelines and storage.
- Establish, document, and enforce best practices for data engineering, version control, CI/CD, and technical documentation, ensuring knowledge is captured and reusable across teams.
- Ensure data security, governance, and compliance standards are built into best practice processes and documentation
- Mentor junior engineers, contribute to technical standards and architecture decisions, and actively support team building, knowledge sharing, and a collaborative engineering culture.
- Any other reasonable duties as required to meet the needs of the business.
Requirements and Qualifications
- At least 5 years experience as Data Engineer or related roles with at least 2–3 years working directly with Microsoft Dynamics 365 data
- Demonstrated experience documenting pipelines, standards, and best practices for both technical and business audiences.
- Understanding of data needs and reporting patterns common in the engineering market sector (e.g., project costing, resource utilization, lifecycle asset data)
- Hands-on experience with Azure data services (Fabric, Data Lake, SQL)
- Advanced SQL skills and experience with data modeling for analytics
- Strong understanding of D365 data models, entities, and integration patterns (Dataverse, Dualwrite, Data Export Service, OData APIs)
- Knowledge of Microsoft Fabric and modern lakehouse architectures
- Knowledge of the engineering market sector, including common data domains, KPIs, and operational workflows (e.g., project-based accounting, asset management, supply chain, or professional services)
