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Added 1 min ago
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Stateside
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Stateside

Senior Engineer Data Enablement & Management (Redshift/Databricks)

Adelphi, MD, United States

$133,000.00 - $148,000.00

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Job details

Pay: $133,000.00 - $148,000.00
Posting language: English
Posted: Nov 6, 2026

Original job description

Senior Engineer Data Enablement & Management (Redshift/Databricks)

Enterprise Architecture, Data, & Integrations

US Exempt Regular

Full time

Stateside Exempt 4.3

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.

The University of Maryland Global Campus (UMGC) is an equal opportunity employer and complies with all applicable federal and state laws regarding nondiscrimination. UMGC is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, ancestry, political affiliation or veteran status in employment, educational programs and activities, and admissions.

Workplace Accommodations:

The University of Maryland Global Campus Global Campus (UMGC) is committed to creating and maintaining a welcoming and inclusive working environment for people of all abilities. UMGC is dedicated to the principle that no qualified individual with a disability shall, based on disability, be excluded from participation in or be denied the benefits of the services, programs, or activities of the University, or be subjected to discrimination. For information about UMGC’s Reasonable Workplace Accommodation Policy or to request an accommodation, applicants/candidates can contact Employee Accommodations via email at employee-accommodations@umgc.edu.

Benefits Package Highlights:

Generous Time Off: Enjoy 22 days of paid vacation, 15 days of sick leave, 3 personal days, and 16 paid holidays (17 during general election years). For part-time employees, time off rates will be prorated based on the number of hours worked.
Comprehensive Health Coverage: Access to health care, medical with vision, dental, and prescription plans for both individuals and families, effective from the 1st of the month following your hire date.
Insurance Options: Term Life Insurance, Accidental Death and Dismemberment Insurance, and Long-Term Disability (LTD) Insurance. Part-time employees working less than 0.5 FTE are not eligible for LTD.
Flexible Spending Accounts: Available for medical and dependent care expenses.
Retirement Plans: Choose between the Optional Retirement Program (ORP) or the Maryland State Retirement and Pension System (MSRPS).
Supplemental Retirement Plans: include 401(k), 403(b), 457(b), and various Roth options. The university does not provide matching funds.
Tuition Remission: Immediate availability for Regular Exempt Staff. Spouses and dependent children are eligible for undergraduate tuition remission after two years of service. NOTE: For part-time employees (at least 50 percent of the time), tuition remission benefits are prorated.

Hiring Range:

$133,000.00 - $148,000.00

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More about this job

Skills

Amazon Redshift
Databricks
Data engineering
Apache Spark
Leadership
Unstructured data
Google Analytics
Business intelligence
Machine learning
Reporting
Troubleshooting
Cost management
Coaching
Process improvement
Analytical skills
Problem solving
Systems thinking
Collaboration
Stakeholder management
Adaptability
SQL
Python
PySpark
Scala
ML
Mlflow
Data warehouse
Oracle JDBC
Unity
Data protection
Microsoft Azure
AWS
CI/CD
DevOps
Computer science
Computer technology
Computer engineering
Oracle PeopleSoft
Brightspace
Salesforce

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