Data Engineer / Lead Data Engineer

CampusWorks is redefining how higher education institutions use technology and functional managed services to drive transformation, financial sustainability, and student success. We serve as a true end-to-end partner to institutional leaders, delivering a fully integrated model that spans professional and managed services. 

We are seeking Data Engineers at both the mid and lead level to build and operate a federated, multi-institution data platform on Azure Databricks and Microsoft Fabric. You will develop the pipelines that move data from Workday and a range of legacy source systems into a governed medallion lakehouse (Bronze / Silver / Gold), delivering clean, reliable, well-modeled data that powers analytics for multiple higher education institutions. 

This posting covers two levels. As a Data Engineer, you will build and maintain production pipelines within established patterns. As a Lead Data Engineer, you will act as a senior technical lead — setting engineering standards, owning complex integrations, mentoring engineers, and reviewing code — as a hands-on individual contributor without direct-report responsibility. Please indicate in your application which level best fits your experience.

Responsibilities

Pipeline development & data modeling 

• Build and maintain batch data pipelines that ingest from Workday (HCM, Finance, Student) and legacy source systems into the Bronze layer 

• Develop transformation logic across Bronze / Silver / Gold layers using Spark, Python, and SQL on Databricks 

• Model data for analytics consumption, balancing performance, reliability, and maintainability 

• Orchestrate and schedule workloads (e.g., Databricks Workflows, Azure Data Factory, or Fabric pipelines) and monitor them for reliability 

• Implement data-quality checks and validation within pipelines 

Platform & governance alignment 

• Work within the federated architecture and Unity Catalog security model, respecting the boundary between institution-administered and centrally managed catalogs 

• Follow the shared transformation standards and reference patterns set by the Solution Architect 

• Contribute to documentation, code reviews, and version-controlled deployment 

Lead-level responsibilities (Lead Data Engineer) 

• Serve as senior technical lead on complex integrations and cross-institution data workstreams 

• Define and enforce engineering standards, patterns, and best practices 

• Mentor and provide technical guidance and code review to other engineers 

• Partner with the Solution Architect and governance leads on design decisions and platform improvements 

The responsibilities listed above are representative of the role and may be adjusted to meet organizational priorities. 

Qualifications & Requirements

• Data Engineer: 3–6 years building production data pipelines. Lead Data Engineer: 7–10 years, with demonstrated technical leadership 

• Hands-on expertise with Apache Spark, Python, and SQL in a Databricks environment 

• Experience designing and building medallion or layered data architectures (Bronze / Silver / Gold or equivalent) 

• Proven experience integrating data from multiple ERP / SIS / operational source systems into an analytics platform 

• Experience with batch pipeline orchestration and scheduling (Databricks Workflows, Azure Data Factory, or similar) 

• Working understanding of data governance, security, and access concepts in a shared or multi-tenant environment 

• Strong problem-solving skills with a consistent focus on data quality and reliability 

• Excellent communication and collaboration skills on a remote team 

• Bachelor’s degree in a technical field or equivalent practical experience 

Preferred Qualifications

• Microsoft Fabric and/or Power BI experience 

• Data-quality tooling such as Great Expectations — expected for the Lead level 

• Experience with higher education source systems: Workday, Banner (Oracle), Colleague (SQL Server), Jenzabar, PowerFAIDS 

• Unity Catalog experience and familiarity with CI/CD for data (Git-based workflows, DevOps pipelines) 

• Exposure to streaming or near-real-time ingestion patterns 

• Databricks certification (e.g., Databricks Certified Data Engineer)