SustainableHR PEO & Recruiting
SustainableHR PEO & Recruiting provided pay range
This range is provided by SustainableHR PEO & Recruiting. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $90,000.00/yr - $110,000.00/yr
We’re partnering with a Madison-based organization that’s investing heavily in a modern data platform to deliver reliable, scalable, and well-governed data across analytics, applications, and integrations.
This
Data Engineer
will play a key role in designing and building production-grade data pipelines, models, and services that support Power BI reporting, APIs, and downstream systems. You’ll collaborate closely with Infrastructure, QA, Database Administration, and application teams to deliver automated, observable, and secure data workflows.
This is a hybrid role. Candidates must be located in the Madison, WI area and able to work on-site as needed.
What You’ll Do
Design and evolve canonical data models, data marts, and lake/warehouse structures
Establish standards for schema design, naming conventions, partitioning, and CDC
Build resilient batch and streaming pipelines using Microsoft Fabric Data Factory, Spark notebooks, and Lakehouse tables
Design and optimize Delta/Parquet tables in OneLake and Direct Lake models for Power BI
Create reusable ingestion and transformation frameworks focused on performance and reliability
Integrations & APIs
Develop secure data services and APIs supporting applications, reporting, and partner integrations
Define and publish data contracts (OpenAPI/Swagger) with versioning and deprecation standards
Partner with DBA and Infrastructure teams to enforce least-privilege access
Infrastructure as Code & DevOps
Author and maintain IaC modules using Bicep/ARM (and where appropriate, Terraform or Ansible)
Own CI/CD pipelines for data, configuration, and infrastructure changes
Collaborate with QA on unit, integration, and regression testing across data workflows
Observability, Reliability & Governance
Implement logging, lineage, metrics, and alerting for pipelines and datasets
Define SLAs for data freshness and quality
Tune Spark performance and manage cloud costs
Apply data quality rules, RBAC, sensitivity labeling, and audit standards
Work cross-functionally with Infrastructure, QA, DBA, and application teams
Contribute to documentation, knowledge sharing, and modern data engineering best practices
What We’re Looking For Required Experience
3+ years building and operating production ETL/ELT pipelines
Apache Spark experience (Microsoft Fabric, Synapse, or Databricks)
Strong T-SQL and Python skills
Streaming platforms such as Azure Event Hubs or Kafka
Change Data Capture (CDC) implementations
Infrastructure as Code and CI/CD (Azure DevOps)
API design for data services (REST/OpenAPI, versioning, authentication)
Preferred Experience
Microsoft Fabric Lakehouse architecture and Power BI Direct Lake optimization
Kusto Query Language (KQL), Eventstream, or Eventhouse exposure
Experience with data lineage, metadata, or cost governance tools
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Base pay range $90,000.00/yr - $110,000.00/yr
We’re partnering with a Madison-based organization that’s investing heavily in a modern data platform to deliver reliable, scalable, and well-governed data across analytics, applications, and integrations.
This
Data Engineer
will play a key role in designing and building production-grade data pipelines, models, and services that support Power BI reporting, APIs, and downstream systems. You’ll collaborate closely with Infrastructure, QA, Database Administration, and application teams to deliver automated, observable, and secure data workflows.
This is a hybrid role. Candidates must be located in the Madison, WI area and able to work on-site as needed.
What You’ll Do
Design and evolve canonical data models, data marts, and lake/warehouse structures
Establish standards for schema design, naming conventions, partitioning, and CDC
Build resilient batch and streaming pipelines using Microsoft Fabric Data Factory, Spark notebooks, and Lakehouse tables
Design and optimize Delta/Parquet tables in OneLake and Direct Lake models for Power BI
Create reusable ingestion and transformation frameworks focused on performance and reliability
Integrations & APIs
Develop secure data services and APIs supporting applications, reporting, and partner integrations
Define and publish data contracts (OpenAPI/Swagger) with versioning and deprecation standards
Partner with DBA and Infrastructure teams to enforce least-privilege access
Infrastructure as Code & DevOps
Author and maintain IaC modules using Bicep/ARM (and where appropriate, Terraform or Ansible)
Own CI/CD pipelines for data, configuration, and infrastructure changes
Collaborate with QA on unit, integration, and regression testing across data workflows
Observability, Reliability & Governance
Implement logging, lineage, metrics, and alerting for pipelines and datasets
Define SLAs for data freshness and quality
Tune Spark performance and manage cloud costs
Apply data quality rules, RBAC, sensitivity labeling, and audit standards
Work cross-functionally with Infrastructure, QA, DBA, and application teams
Contribute to documentation, knowledge sharing, and modern data engineering best practices
What We’re Looking For Required Experience
3+ years building and operating production ETL/ELT pipelines
Apache Spark experience (Microsoft Fabric, Synapse, or Databricks)
Strong T-SQL and Python skills
Streaming platforms such as Azure Event Hubs or Kafka
Change Data Capture (CDC) implementations
Infrastructure as Code and CI/CD (Azure DevOps)
API design for data services (REST/OpenAPI, versioning, authentication)
Preferred Experience
Microsoft Fabric Lakehouse architecture and Power BI Direct Lake optimization
Kusto Query Language (KQL), Eventstream, or Eventhouse exposure
Experience with data lineage, metadata, or cost governance tools
#J-18808-Ljbffr