Senior Data Engineer, Fabric Platform

IES Holdings; Houston (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

IES Holdings is seeking a Data Engineer IV within the ESS team to build and operate data pipelines in Microsoft Fabric, ingesting ERP/HRIS/safety/financial data across multiple OpCos. You will design, test, and coordinate data operation enhancements while ensuring governance and auditability.

The role emphasizes hands-on development, CI/CD, and collaboration with PM, IT infra, and applications teams to deliver production-grade data platforms.

Qualifications

• 5+ years hands-on data engineering experience in production ETL/ELT pipelines.
• Experience with data governance and audit-ready documentation.

Responsibilities

• Build and maintain bronze ingestion, silver transformation, and gold serving data orchestration.
• Implement incremental/watermark-based and upsert ingestion patterns from source ERP systems.
• Own table, pipeline, and notebook builds following medallion architecture and naming conventions.
• Support Fabric deployment pipelines and CI/CD automation; ensure auditability.

Skills

ETL/ELT pipelines

T-SQL

Databricks/Spark

Git

CI/CD

Delta Lake

Star schema

Microsoft Fabric

Education

Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related field

Tools

Microsoft Fabric

Azure Synapse Analytics

Azure Data Factory

Databricks/Spark

Job description

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, DirectLake 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 DevOps 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.

PREFERRED QUALIFICATIONS

• Working proficiency in PySpark or Python for notebook-based transforms.
• Direct production experience with Microsoft Fabric specifically — lakehouses, Dataflow Gen2, Fabric deployment pipelines, OneLake.
• Familiarity with Dynamics 365 Finance & Operations, Dataverse, or CDM-aligned schemas
• Experience with Azure DevOps pipeline authoring and the Fabric REST API.
• Experience supporting a multi-entity or multi-subsidiary ERP consolidation (shared gold schema across divergent source configurations).
• Experience building or contributing to a data dictionary, metadata catalog, or ontology-aligned naming standard.

CORE COMPETENCIES

• Technical depth over breadth — comfortable being the person who actually builds and debugs the pipeline, not just designs it on a whiteboard.
• Documentation discipline — flags known gaps and open risks explicitly rather than presenting a clean narrative that hides them.
• Self-direction — able to execute against an established architecture with minimal oversight, while still raising well-reasoned objections where necessary.
• Attention to detail with financial/ERP data to minimize downstream consequences.
• Adaptability — comfortable working against an architecture that is actively being built and refined, not a finished spec.