MS Fabric Data Engineer

Vaco has a great opportunity for an MS Fabric, Data Engineer for our client in Northern Cincinnati. This role is 100% onsite in office. This position requires eligibility for a U.S. security clearance. U.S. citizenship is required in order to obtain the necessary clearance. Position Summary Our client is seeking a hands‑on Data Engineer to stand up and scale our enterprise Azure data platform and Microsoft Fabric environment in support of a major business integration and analytics transformation initiative. This role will be central to unifying data across two legacy operating companies as we implement our future ERP systems. The Data Engineer will design, build, and support the pipelines, data structures, quality controls, security model, and semantic layers needed to deliver trusted executive KPI reporting, cross‑functional analytics, and future AI‑enabled business insights. The environment includes multiple enterprise business platforms across manufacturing, service, supply chain, sales, and finance. This is not a report developer role. This is a platform building role for a technically strong engineer who can work across systems, business functions, and data domains. Key Responsibilities Build Modern Analytics Platform Enhance Azure and Microsoft Fabric, including data ingestion, storage, transformation, modeling, and governed delivery patterns. Build and maintain robust automated pipelines from business applications, supplier/customer portals, and operational databases into a unified data environment. Establish raw, curated, and business‑ready data layers to support repeatable analytics, executive dashboards, and self‑service reporting. Integrate and normalize data from Epicor, JobBOSS, SAP ECC, Salesforce, Azure SQL, and other internal and external business systems. Create reusable data models, dimensions, facts, and business logic to support quote‑to‑cash, procure‑to‑pay, inventory, manufacturing, service, finance, and KPI reporting. Implement data quality checks, exception handling, reconciliation routines, and monitoring to improve trust in reporting and reduce manual work. Support Power BI delivery through governed semantic models, dataset optimization, security configuration, and drill‑through to transaction‑level source data. Help define and implement role‑based access, row‑level security, object‑level security, and workspace governance aligned to company policy and regional privacy requirements. Document source‑to‑target mappings, lineage, business rules, data definitions, and engineering standards. Evaluate the current integration landscape, including manual feeds, direct SQL access, and middleware processes, and help transition the organization to more controlled and scalable patterns. Required Experience & Qualifications 2+ years of experience in data engineering, data integration, or enterprise data platform development. 2+ years of hands‑on experience with Azure data services such as Azure Data Factory, Azure SQL, Azure Data Lake, Microsoft Fabric, Synapse, or closely related Microsoft data technologies. Strong SQL development skills and experience optimizing complex data transformations. Experience with Python, PySpark, Spark SQL, or comparable scripting/programming tools used in modern data engineering. Experience designing and implementing data lake, lakehouse, or data warehouse solutions. Strong understanding of dimensional modeling, semantic modeling, and data structures used for reporting and analytics. Experience building and supporting ETL/ELT pipelines across multiple source systems. Experience with Power BI datasets/semantic models and the data engineering practices that support governed reporting environments. Experience with data quality, reconciliation, lineage, and documentation practices. Ability to work in a fast‑moving environment where source data, requirements, and processes are still maturing. Strong communication skills and the ability to work directly with both technical teams and business leaders. Education Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field is preferred. Equivalent practical experience in enterprise data engineering will also be considered. Key Competencies Strong engineering discipline and attention to data accuracy Ability to structure ambiguous problems into practical technical solutions Business‑facing communication and stakeholder management Systems thinking across data, process, and platform layers Ownership mindset and follow‑through Comfort operating in a hands‑on build environment rather than a fully mature data organization This role is critical to creating a single, trusted data foundation across the business. The Data Engineer will help move the organization from fragmented, manually maintained reporting toward a governed, scalable platform that supports executive decision‑making, self‑service analytics, and future AI‑enabled insight generation. #J-18808-Ljbffr