Engineering Manager - GRC

Workiva Inc.•Ames, IA

9d•$129,000 - $210,000

About The Position

The Engineering Manager for GRC Issue Management is accountable for the health of the delivery pipeline, the growth of engineers, and the quality standards of the team. The role owns engineering decisions, architecture, resourcing, and the issue-management lifecycle across GRC solutions, advancing AI-assisted remediation while ensuring actions, decisions, and evidence remain traceable and defensible to auditors and regulators. The manager leads and develops engineers, owns delivery planning and risk communication, and works closely with Product, Design, UX, and group leadership to shape product direction and priorities. The role also oversees shared issue-management capabilities, integration contracts, audit history and evidence infrastructure, and engineering quality—including the evaluation, review, and governance of AI-assisted work. Workiva supports employees working from an office or remotely from any location within their country of employment. Remote work requires reliable internet access. Travel of up to 25% may be required for team and corporate meetings.

Requirements

Bachelor's degree in a related field with at least 5 years of relevant engineering experience, or equivalent experience in lieu of a degree.
At least 3 years of direct engineering management experience, 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 approach 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 used 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 and comfort with 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 and assign roles and tasks based on 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 maintain consistent patterns across GRC solutions in partnership with Product and Design.
Drive development of issue and action plan management, workflows and approvals, notifications and escalation, evidence capture, and public APIs.
Build and maintain audit-trail, history, and evidence infrastructure that makes remediation status trustworthy and defensible.
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, recognizing contribution and growth.
Conduct regular one-on-ones, coach engineers on career and technical development, and address performance concerns when needed.
Build a positive team environment, resolve conflict directly, and create opportunities aligned with employees' growth goals.
Participate in recruiting, selection, and onboarding.
Partner with the Product Manager and UX Lead to shape domain direction and define integration contracts with upstream solutions.
Represent team resourcing needs and priorities to group leadership with clear tradeoffs.
Partner with group leaders to define and plan team and group objectives.
Identify technical risks and communicate them clearly to leadership before they become fires.
Build AI-assisted workflows while maintaining manual paths for practitioners.
Ensure AI-generated outputs are explainable, auditable, and traceable to source data, with a human accountable for every decision.
Maintain a quality bar for AI-generated content appropriate for boards, audit committees, and regulators.
Evaluate engineering output based on judgment and outcomes rather than volume, distinguishing skill growth from AI-assisted output.
Ensure code reviews 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 reason and work without it.