Sr. Solutions Engineer

Evergreen Goodwill•Seattle, WA

11d•Hybrid

About The Position

Evergreen Goodwill is seeking a Sr. Solutions Engineer to own a domain of the systems our organization runs on — the platforms, integrations, and automations that keep a 2,000-person nonprofit operating across 26 locations. This is an internal-facing engineering role, not a pre-sales position. You will take ownership of a set of business-critical systems: how they are designed, how they connect to everything else, and how they behave when something breaks. We are modernizing much of our technology estate. You will be designing and building replacements, not maintaining a finished stack — and you will own the standards for how that work gets done in your domain. This is a senior individual contributor role with real decision authority. You will work directly with the business areas you support, and you will be the person who decides how the systems in your domain fit together. Where your authority sits. You decide within the systems you own. Standards that span domains — how your systems talk to everyone else's, how things get deployed and changed — sit with our Enterprise Solutions Architect, and you help shape them. Inside your domain, the calls are yours.

Requirements

8+ years in solution engineering, systems integration, business systems, or software engineering
Demonstrated ownership of a platform or system domain — the architecture, the standards, and the consequences when it breaks. Not contribution to someone else's domain. This is the threshold requirement and we will probe it directly: what you owned, what you decided, what you were accountable for
Evidence you set technical practice that other engineers followed — configuration standards, integration patterns, release discipline, or review practice adopted by people who did not report to you
Current hands-on development. You should be able to read a codebase, write production code, and be credible in a technical design review. Configuration-only backgrounds will not clear this bar
Ownership of a production codebase or internally built service, including what happened when it failed
Production experience building with AI, not just using it. Large language models or classification models integrated into a working system — prompt and context design, retrieval patterns, structured output, and evaluation. You should be able to explain how you measured whether the model was actually right, and where the approach broke down
Judgment about when AI is the wrong tool. We are looking for someone who has shipped it and also killed it
Deep systems integration experience: API design, interface contracts, message and event patterns, and the failure modes of each
Experience delivering work that required another department to change how it operates
Experience owning reliability for an operationally critical system, including on-call and incident response
Clear written communication, and the ability to explain a technical constraint to a non-technical audience without hand-waving
Experience in an environment where systems are being replaced rather than maintained

Nice To Haves

Experience in an environment with physical operations and frontline staff — retail, logistics, warehousing, manufacturing, or similar
ERP, finance, workforce management, or point-of-sale system experience
Payment processing or PCI-scoped system experience
Operational or industrial automation experience
Experience with internally built software alongside commercial platforms
Agentic or tool-using AI systems in production — orchestration, guardrails, human-in-the-loop design
Model selection and cost/performance tradeoff analysis across providers
Computer vision or automated classification applied to physical operations
Nonprofit, public sector, or mission-driven experience
Experience mentoring less experienced engineers

Responsibilities

Own the architecture and forward plan for a domain of business-critical systems
Own the integrations between your domain and the rest of the enterprise estate — finance, workforce, inventory, and reporting
Own configuration standards, release practice, and operational handoff for your domain, working within enterprise architecture standards
Design, build, and deploy integrations, automations, and configuration across the domain
Own production codebases where the domain includes internally built software
Build AI into the systems you own where it earns its place — classification, extraction, retrieval, and language-model services wired into real workflows, with evaluation against the task rather than adoption by default
Tier 3 diagnosis for the systems you own — the escalation point, not an escalation path
Own reliability and service levels for the systems in your domain, including monitoring, alerting, and incident response
Design the operational handoff so systems support teams can run what you build
Participate in change and maintenance windows, including business-sensitive timing
Partner directly with the business areas you support. Translate operational need into system capability without a translator in between
Say no with a reason and an alternative when a request would compromise reliability or integrity
Manage vendor relationships for platforms in the domain, including evaluation and selection input
Raise the capability of the solution engineering team through review, standards, and teaching
Mentor less experienced engineers, including on the domain you own
Document your work so someone else can carry it