Senior Staff Software Engineer

Workiva Inc.•Remote - IA, IA

8d•$151,000 - $242,000•Hybrid

About The Position

At Workiva, we build and scale an enterprise platform that powers complex financial reporting, compliance, and collaboration for some of the world’s largest organizations. We solve for high-throughput data, strict consistency, and architectural scale, while exploring how AI can improve the way engineering teams design, build, and operate software. We are not simply layering AI onto existing workflows. We are evolving how engineering gets done. That means building systems, practices, and products where AI is a real collaborator in development, testing, validation, and platform evolution. We’re hiring a Senior Staff Software Engineer who can lead across complex technical work, act as a force multiplier for other engineers, and help shape how teams build in an AI-enabled environment. This is a hands-on role for someone who can contribute directly to design and implementation while also raising the technical bar across the organization.

Requirements

6+ years of experience building and scaling enterprise software systems.
Experience leading technical strategy and architecture across teams.
Hands-on experience with agentic AI development, including shipping real work with AI as a primary development collaborator.

Nice To Haves

Experience driving high-impact initiatives across multiple teams
Experience improving engineering quality through TDD, BDD, load testing, stress testing, chaos testing, invariant testing, mutation testing, or similar approaches.
Strong systems thinking and judgment, including awareness of second-order effects.
Demonstrated proficiency in at least two delivery modes: platform modernization, iterative product evolution, and high-ambition greenfield development.
Ability to operate as a force multiplier for other engineers.
Depth in a domain that materially improves system design or developer experience.
Proven experience operating at Staff+ level across multiple teams or domains.
Strong background in distributed systems and platform architecture.
Experience inferring behavioral specifications from existing systems through code, tests, logs, and subject-matter experts.
Ability to deliver production-quality outcomes in a small, time-boxed team setting.
Strong written communication skills and a habit of creating useful, durable artifacts.

Responsibilities

Deliver outcomes: Partner with engineering teams to co-deliver complex work across platform modernization, product evolution, and new product initiatives. Adapt your approach to match the needs of the work, whether the priority is speed, scale, or long-term maintainability.
Use AI-native development practices to build automation, validation, and engineering harnesses that improve quality and leverage.
Help establish thoughtful code review and pre-production quality practices that support fast, reliable delivery.
Execute technical strategy: Lead technical direction for large, cross-team initiatives aligned to long-term platform strategy.
Design systems that balance flexibility, scalability, and maintainability.
Anticipate future constraints and evolve architecture before problems reach scale.
Identify and resolve performance, reliability, and architectural bottlenecks.
Drive early-stage exploration for ambiguous problems, including work where the answer becomes clear through prototyping and building.
Develop AI systems and platform enablement: Build infrastructure that embeds AI into engineering workflows.
Design tools and patterns that reduce manual effort through automation and intelligent assistance.
Create guardrails that support safe, reliable AI-assisted development.
Help define system behavior by capturing specifications from code, tests, logs, and the people closest to the work.
Influence and lead: Raise the technical ceiling of the teams around you.
Drive alignment across teams and influence key technical decisions.
Reduce ambiguity and help teams make better decisions faster.
Create durable artifacts such as runbooks, decision logs, and tests that can be handed off and evolved by others.
Share context across product domains to help solve problems more effectively.