Senior Data Engineer, Fabric Platform

The Data Engineer IV is a senior, individual-contributor role embedded in the IES Enterprise Software Solutions (ESS) team. This is a hands-on build role: the person will work directly inside Microsoft Fabric to construct and operate a medallion structure backend to ingest, transform, and serve data from ERP, HRIS, safety, and financial reporting source systems across multiple operating companies (OpCos). IES created this position add engineering capacity to ESS’ current high-demand analytics development and delivery, along with an active, in-flight Fabric implementation.

This role executes and extends the designed data architecture, as well as designing, testing, and coordinating approval for data operation enhancements.

Security and governance are a necessity throughout all aspects of work in this role. All development will require audit-ready documentation as well as enterprise-level CI/CD. The candidate must also be able to collaborate with project management, IT infrastructure and IT applications teams for planned development releases

KEY RESPONSIBILITIES

• Build and maintain bronze ingestion, silver transformation, and gold serving data orchestration against the Fabric medallion architecture.
• Implement incremental/watermark-based and upsert ingestion patterns to correctly capture retroactive record changes from source ERP systems.
• Apply the team's Dataflow Gen2 vs. notebook/stored-procedure heuristic — use Dataflow Gen2 for straightforward column-level transforms; use PySpark/SQL notebooks for multi-table joins, upsert/merge logic, and any gaps in Dataflow Gen2 capability.
• Diagnose and resolve schema drift, Direct Lake cross-workspace rebinding issues, and other data layer engineering problems.
• Own table, pipeline, and notebook builds against the team's established naming conventions and medallion layer standards.

Platform & CI/CD

• Support and extend the Fabric deployment pipeline (Engineering → Test → Prod), including closing known gaps:
  Git integration, automated rollback, and connection/service-principal ownership ahead of production promotion.
• Contribute to CI/CD automation including but not limited to Azure Dev Ops pipeline orchestration and Fabric REST API integration.
• Maintain and troubleshoot centrally managed Fabric connections and gateways; support the pre-production connection ownership and service-principal audit.
• Document deployment gaps and open engineering risks formally to plan and track their resolution.

Semantic Layer & Data Contract Support

• Build and maintain the gold-layer tables and refresh patterns that underpin the team's tiered semantic model architecture (enterprise, OpCo-simple, OpCo-complex tiers).
• Support the shared Gold pipeline / parameterized deployment pattern used to onboard additional OpCos onto the platform.
• Contribute engineering input to the data dictionary and metadata standards that underpin future AI/agent readiness — field lineage, entity definitions, and naming that map cleanly to an ontology layer.

Governance & Data Handling

• Maintain the accuracy and reliability of production data pipelines feeding financial and operational reporting across OpCos.
• Handle multi-OpCo financial and ERP data with appropriate discretion and data-integrity rigor.
• Keep the data source registry and related governance artifacts current as new sources and OpCos are onboarded.

REQUIRED QUALIFICATIONS

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent hands-on experience.
• 5+ years of hands-on data engineering experience — building and operating ETL/ELT pipelines in production, not just querying or reporting against existing data.
• Strong T-SQL: complex joins, query optimization, stored procedures, schema design.
• Practical experience with:
• Microsoft Fabric, or direct equivalent experience with Azure Synapse Analytics, Azure Data Factory, and Databricks/Spark that transfers cleanly to Fabric's lakehouse/warehouse model.
• Medallion (bronze/silver/gold) architecture and Delta Lake table formats.
• Dimensional/star-schema modeling (facts, dimensions, surrogate keys).
• Git-based version control and basic CI/CD concepts for data platforms.

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