Applications Systems Manager I – EHRA

State of North Carolina•Wake Forest, NC
•$91,600 - $160,299•Hybrid

About The Position

The Applications Systems Manager I leads and prioritizes the data engineering team’s work supporting NCDOT data pipelines, integrations, and related platform activities. The role promotes consistent development practices, predictable delivery, and effective communication with leadership, and leads data stewardship activities to establish shared data definitions, metadata standards, and data quality expectations. The position supervises and coordinates data engineering staff, manages pipeline and integration development and maintenance in Databricks, and works with senior subject-matter experts on complex engineering and architectural decisions. It removes blockers, escalates risks, tracks delivery, shares status with leadership, and coordinates across technical and business teams. The role also oversees data discovery and stewardship capabilities, including data cataloging, metadata management, lineage documentation, and data quality practices, while coordinating data stewards and aligning stewardship work with enterprise governance objectives. The position is in the NCDOT Web Systems unit, which directs Internet- and Intranet-based application development, reporting, server and system architecture, and web system maintenance. The position may be eligible for hybrid remote work under state and agency policy, but requires weekly onsite work. Telework arrangements may be ended by the employer at its discretion. The role is designated Statutory Exempt (EHRA) and is exempt from the State Human Resources Act.

Requirements

  • Bachelor’s degree in computer science, an IT-related field, or another related technical degree from an appropriately accredited institution, and five years of progressive experience, including at least two years in programming and applications analysis or related work; OR an associate degree in one of those fields and six years of progressive experience, including at least two years in programming and applications analysis or related work; OR an equivalent combination of education and experience.

Nice To Haves

  • Experience supervising technical staff, setting priorities, coordinating workloads, supporting employee development, and preparing clear status reports for leadership.
  • Skill applying Agile methods such as Scrum or Kanban to manage sprints, refine backlogs, assign tasks, track progress, and support predictable delivery.
  • Knowledge of cloud-based data pipeline environments such as Databricks or Azure Data Lake, and knowledge of data definitions, metadata practices, data quality standards, and stewardship processes across business and technical teams.

Responsibilities

  • Supervise and coordinate data engineering staff, including prioritization, sprint planning, backlog refinement, and task assignment.
  • Oversee development and maintenance of data pipelines and integrations within Databricks, ensuring adherence to standards and documentation.
  • Partner with senior subject-matter experts on complex engineering and architectural decisions to support reliable, accurate, maintainable solutions.
  • Remove blockers, escalate risks, and keep work aligned with NCDOT priorities and project timelines.
  • Provide regular status updates covering backlog health, progress, risks, and delivery forecasts.
  • Facilitate communication across technical and business teams to support planning and resource alignment.
  • Maintain operational documentation and promote repeatable development processes.
  • Lead and manage data discovery and stewardship capabilities, including data cataloging, metadata management, lineage documentation, and data quality practices.
  • Coordinate data stewards and collaborate with business and technical partners to ensure consistent interpretation and use of shared data assets.
  • Identify documentation and definition gaps, prioritize stewardship tasks, and align efforts with enterprise data governance objectives.
  • Ensure data engineering outputs adhere to enterprise data quality, metadata, and stewardship standards.
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