Edtech.com's Summary

University of Maryland Global Campus is hiring a Senior Engineer Data Enablement & Management (Redshift/Databricks). The role leads the design, modernization, and day-to-day reliability of the university's enterprise Lakehouse and Data & AI platform, a unified system for storing, governing, and analyzing data built on Databricks, Delta Lake, and Apache Spark. The engineer also mentors other engineers and works with the data architecture, data science, business intelligence, and application engineering teams to deliver secure, trusted data capabilities.

Highlights

Lead the design, optimization, and operation of enterprise data engineering capabilities on the Data & AI platform, setting engineering standards, reusable frameworks, and automation practices.
Develop and modernize enterprise data pipelines, Lakehouse components, and data products that handle structured, semi-structured, and unstructured data.
Guide complex migration efforts, including the transition from Redshift to Databricks.
Lead the engineering behind Databricks AI/BI dashboards, Genie Agents, semantic models, and governed data marts that support analytics and self-service reporting.
Implement data quality frameworks, metadata management, lineage, observability, and security controls that meet compliance, privacy, and auditability requirements.
Provide engineering leadership for platform monitoring, performance tuning, incident management, and cost management across enterprise data environments.
Mentor engineers and contribute to design reviews, technical evaluations, and platform best practices.
Bachelor's degree in computer science, information systems, data science, or a related technical field (or equivalent experience) and 5+ years of data engineering experience, including leading enterprise Databricks, Delta Lake, and Spark Lakehouse implementations; a Master's degree and 8+ years are preferred.
Advanced SQL, Python/PySpark, and Scala skills plus experience with Unity Catalog and Azure and AWS data platform services; integrating PeopleSoft, D2L, Salesforce, and Workday data is preferred.
Hiring range of $133,000 to $148,000 for a hybrid role that requires on-site presence in Adelphi, Maryland.

Senior Engineer Data Enablement & Management (Redshift/Databricks) Full Description

Senior Engineer Data Enablement & Management (Redshift/Databricks)

Central IT Admin | Exempt Regular 4.3

Location: Adelphi, MD (Hybrid, On-Site Presence Required)

The Senior Engineer, Data Enablement & Management is responsible for leading the design, implementation, modernization, and operational reliability of UMGC's enterprise Lakehouse and Data & AI platform. This role serves as a senior technical leader for enterprise data engineering, data platform operations, and advanced analytics enablement, providing expertise across Databricks, Delta Lake, Apache Spark, AI/BI capabilities, and cloud data architectures. The Senior Engineer leads complex data initiatives, mentors engineering staff, establishes engineering standards, and partners closely with Data Architecture, Data Science & AI, Business Intelligence, Application Engineering, and business stakeholders to deliver scalable, secure, and trusted enterprise data capabilities.

Duties and Responsibilities:

Enterprise Data Engineering & Technical Leadership.

Lead the design, implementation, optimization, and operational maturity of enterprise data engineering capabilities across the Data & AI platform. Establish engineering standards, reusable frameworks, architectural patterns, automation practices, and operational procedures that support scalable and reliable enterprise data services.

Data Integration, Lakehouse & Platform Modernization.

Lead development and modernization of enterprise data pipelines, Lakehouse architecture components, data products, and integration capabilities supporting structured, semi-structured, and unstructured data.
Guide complex migration and modernization efforts, including Redshift-to-Databricks transitions and enterprise data platform transformation initiatives.

AI, Analytics & Data Product Enablement.

Lead engineering efforts supporting Databricks AI/BI dashboards, Genie Agents, Genie One, semantic models, governed data products, data marts, and advanced analytics capabilities.
Ensure platform capabilities effectively support business intelligence, AI, machine learning, operational analytics, and self-service reporting objectives.

Data Governance, Quality & Security Engineering.

Lead implementation of data quality frameworks, metadata management, lineage capabilities, observability controls, security policies, and governance standards.
Ensure enterprise data assets meet organizational requirements for trust, compliance, auditability, privacy, and operational reliability.

Platform Operations, Reliability & Performance Optimization.

Provide senior-level engineering leadership for platform monitoring, resilience, performance optimization, troubleshooting, incident management, and operational support.
Lead efforts to improve service reliability, scalability, efficiency, cost management, and operational maturity across enterprise data environments.

Mentoring, Innovation & Continuous Improvement.

Mentor engineers and technical resources while contributing to engineering standards, design reviews, technical evaluations, and platform best practices.
Evaluate emerging technologies and automation opportunities that advance organizational data, AI, and analytics capabilities.
Other duties as assigned.

Competencies:

Advanced data engineering and Lakehouse expertise
Technical leadership and engineering excellence
Enterprise data architecture implementation
Data quality, governance, and security awareness
Analytical thinking and complex problem solving
Systems thinking and platform optimization
Automation and operational excellence
Collaboration and stakeholder engagement
Mentoring and knowledge sharing
Accountability and results orientation
Innovation and continuous improvement
Adaptability and continuous learning.

Skills:

Databricks Data Intelligence Platform
Apache Spark, Delta Lake, and Medallion Architecture
Enterprise data engineering and pipeline development
Databricks AI/BI dashboards, Genie Agents, and Genie One
Advanced SQL, Python/PySpark, and Scala
AI/ML enablement and MLflow operations
Data product engineering and semantic model development
Lakehouse, data warehouse, and data lake architecture implementation
Data integration, APIs, JDBC, cloud storage, and orchestration
Unity Catalog, metadata, lineage, and governance technologies
Data quality, observability, monitoring, and automation
Zero Trust security controls and data protection
Azure and AWS data platform services
CI/CD, DevOps, DataOps, AIOps, and MLOps
Performance tuning, scalability, and cost optimization
Technical mentoring and engineering governance.

Education & Experience Requirements:

Education:

Bachelor's Degree in Computer Science, Information Systems, Software Engineering, Data Science, Information Technology, Computer Engineering, or a related technical discipline or equivalent combination of education and experience.

Experience:

5+ years of progressive experience in data engineering, data platform engineering, analytics engineering, database engineering, or related disciplines.
Experience leading enterprise-scale Databricks, Delta Lake, Spark, and Lakehouse implementations.
Experience designing and operating enterprise data pipelines, governed data products, and analytical platforms.
Experience supporting Databricks AI/BI capabilities, semantic models, Genie Agents, and advanced analytics solutions.
Experience implementing enterprise data governance, observability, security, and operational controls.
Experience leading modernization, migration, automation, and platform optimization initiatives.
Experience mentoring engineers and serving as a senior technical resource for data platform initiatives.

Certifications:

Not required.

Preferred Experience Requirements:

Education:

Master's Degree in Computer Science, Information Systems, Software Engineering, Data Science, Information Technology, Computer Engineering, or a related technical discipline (or equivalent combination of education and experience).

Experience:

8+ years of progressive experience in enterprise data engineering, cloud data platforms, analytics engineering, AI enablement, or Lakehouse operations.
Experience serving as a Senior Data Engineer, Lead Data Engineer, Data Platform Engineer, Analytics Engineer, or similar senior technical role.
Experience leading enterprise data modernization, Lakehouse transformation, AI enablement, and cloud migration initiatives.
Experience integrating PeopleSoft, D2L, Salesforce, Workday, ERP, CRM, LMS, and SaaS platforms into enterprise data ecosystems.
Experience implementing advanced governance, metadata, lineage, MDM, AI/ML, and observability capabilities.
Experience working within higher education or similarly complex enterprise environments preferred.

Certifications:

Databricks Certified Data Engineer Professional
Databricks Certified Machine Learning Professional
Azure Data Engineer Associate
Microsoft Fabric Analytics Engineer Associate
AWS Certified Data Engineer Associate
AWS Solutions Architect Associate or Professional
ITIL Foundation or advanced ITSM certification.

All submissions should include a cover letter and resume.

Hiring Range:

$133,000.00 - $148,000.00

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Senior Engineer Data Enablement & Management (Redshift/Databricks)

Edtech.com's Summary

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