Data Engineer

We are looking for a hands-on Data Engineer to help build and expand an enterprise data platform in Mason, Ohio. This role will focus on creating a scalable Azure and Microsoft Fabric environment that brings together information from multiple business systems to support reliable reporting, analytics, and future data-driven innovation. The position is ideal for someone who enjoys designing core data architecture, improving data quality, and enabling business teams with trusted insights across manufacturing, service, supply chain, sales, and finance.

Responsibilities:

• Design and develop a modern enterprise data ecosystem using Azure and Microsoft Fabric, covering ingestion, storage, transformation, and delivery for analytics use cases.
• Create and support automated data pipelines that collect information from operational applications, external portals, and databases into a centralized environment.
• Structure raw, refined, and business-ready data layers so teams can access consistent data for dashboards, reporting, and self-service analysis.
• Consolidate and standardize data from platforms such as Epicor, JobBOSS, Salesforce, and other internal or third-party systems used across the organization.
• Build reusable data models and business logic that support reporting across order management, procurement, inventory, manufacturing operations, service, and finance.
• Introduce data validation, reconciliation, monitoring, and error-handling processes to strengthen data accuracy and reduce manual correction efforts.
• Partner with reporting teams by enabling governed semantic models, optimizing datasets, and supporting secure access to detailed transactional data in Power BI.
• Define and apply security controls, including role-based permissions and data access rules, in alignment with internal governance standards and privacy expectations.
• Maintain clear technical documentation for mappings, lineage, transformation rules, data definitions, and engineering standards.
• Assess existing integration methods, including manual and legacy approaches, and help implement more scalable and controlled data delivery patterns over time.