Engineering Manager - GRC

Workiva•Ames, IA

8d•$129,000 - $210,000

About The Position

The Engineering Manager for GRC Issue Management is accountable for delivery pipeline health, engineer development, and the team's quality bar. The role owns engineering decisions, architecture, and resourcing for a domain focused on a consistent remediation lifecycle across GRC solutions. The team is building toward AI-assisted triage, routing, and issue tracking, with actions, decisions, and evidence that remain traceable and defensible to auditors and regulators. The manager leads engineering delivery and people development, partners with Product and Design on domain direction, and defines clear integration contracts with upstream solutions. The role also guides AI-era engineering practices, ensuring AI-assisted workflows retain manual paths, outputs are explainable and auditable, and code review and quality gates identify errors and security gaps. Workiva supports working remotely or from an office within the employee’s country of employment; reliable internet access is required for remote work, and travel of up to 25% is expected.

Requirements

Bachelor’s degree in a related field with 5+ years of relevant engineering experience, or equivalent experience in lieu of a degree.
3+ years directly managing engineers, including supervisory experience of supervisors and/or individual contributors.
Willingness to travel up to 25% for team and corporate meetings.
Reliable internet access for remote work.

Nice To Haves

Ability to manage complex projects, set priorities, and meet deadlines in a high-compliance environment.
People-first mindset and experience in career development, performance coaching, and building team culture.
Experience in GRC, compliance, audit, or risk management, or similar high-compliance, high-stakes product domains.
Experience building shared platform capabilities consumed by multiple product teams.
Experience with workflow, state-machine, or case-management systems.
Experience evaluating engineering output in an AI-augmented environment and distinguishing genuine skill growth from AI-assisted volume.
Strong communication skills, including conflict resolution and facilitating difficult discussions.
Grounding in SDLC, computer science fundamentals, data structures, algorithms, and object-oriented design.
Experience with automated testing or SDLC process improvement.
Experience in an Agile development environment.

Responsibilities

Lead a team of engineers, assigning roles and tasks according to team goals and individual growth areas.
Own the team’s delivery schedule and communicate risks to group leadership early.
Own the issue management lifecycle and consistent patterns across GRC solutions in partnership with Product and Design.
Drive development of issue and action plan management, workflows and approvals, notifications and escalations, evidence capture, and public APIs.
Build and maintain audit trail, history, and evidence infrastructure for trustworthy, defensible remediation status.
Define and execute product quality strategy with group leaders and individual contributors.
Reinforce company values, product development standards, and agile practices across the team.
Assess and document individual performance, conduct regular 1:1s, coach engineers, and address performance issues when needed.
Build a positive, engaged team environment, resolve conflict, and create career-growth opportunities for reports.
Participate in recruiting, selection, and onboarding.
Represent engineering alongside Product and UX leadership in shaping domain direction.
Define integration contracts for upstream solutions and manage collaboration across integration points.
Represent team resourcing needs and priorities to group leadership, communicating tradeoffs.
Partner with group leaders to define and plan team and group objectives.
Identify technical risks and communicate them to leadership early.
Build AI-assisted workflows while retaining manual paths for practitioners.
Ensure AI-generated outputs are explainable, auditable, and traceable to source data, with human accountability for decisions.
Maintain quality standards for AI-generated content appropriate to high-stakes GRC audiences.
Evaluate engineering output based on judgment and outcomes rather than volume, distinguishing skill growth from AI-assisted output.
Ensure code review and quality gates catch hallucinated logic, errors, and security gaps in AI-generated code.
Coach engineers to use AI effectively while retaining the ability to work and reason without it.