Product Engineer

WorldStrides is the global leader in educational travel and experiential learning. The company was founded in 1967 to provide middle school travel programs to Washington, D.C. and has grown to provide a wide range of programs for more than half a million students annually to over 100 countries around the world. WorldStrides offers experiential learning programs in educational travel, performing arts, language immersion, career exploration, service-learning, study abroad, and sports. Each of these experiences helps students to see beyond the classroom and to see the world – and themselves – in new ways.

A hybrid builder who finds the highest-leverage problems across WorldStrides, prototypes working solutions with AI-assisted development, and hands them off to engineering for enterprise scale.

Product Engineers are part product manager, part developer, and part solutions architect. They embed with teams across the business — operations, sales, marketing, finance, customer support, academics, and beyond — find where the company can create value, and then build it. That value might be a brand-new customer experience, a new digital capability, a smarter platform feature, or a redesigned internal process. They own the outcome, not the ticket.

Product Engineers deploy wherever the leverage is. The work is the same wherever it lands: get close to the real problem, find the leverage, build the proof, and graduate it to production with engineering.

Discovery — find the leverage.

• Embed with stakeholders across lines of business to understand how work actually gets done and where the real opportunity is — whether that's an unmet customer need, a missing capability, or a broken workflow.
• Identify and frame opportunities, then qualify them: is the problem worth solving — material, real, measurable, and feasible to build?
• Build the case for the work — quantify the prize, whether that's revenue or growth, a better customer experience, or hard savings, cost avoidance, and capacity released — and pressure-test it with finance and product partners before committing the team's time.
• Design and build both to support proof-of-concept and pilot — working software, not slideware.
• Use AI-assisted development to move fast: speed and learning matter more than polish at the POC stage.
• Connect prototypes to real systems of record (i.e., Dataverse, in-house systems) through approved integration patterns, reading and writing only via sanctioned product-owner requests.
• Run pilots with real users, instrument outcomes, and iterate against evidence.

Hand off — land the plane with IT, not around it.

• Build to be portable from day one: clean, reviewable code that engineering can adopt rather than rebuild.
• Partner with the IT organization to graduate prototypes to production, and leverage org-wide DevOps and SDLC (trunk-based development, PR and peer review, CI/CD, observability) as those capabilities mature.
• Document architecture, decisions, and assumptions so the work survives the handoff.

Feed the system.

• Every project is a live R&D loop. Surface the patterns, edge cases, and insights you find so they shape the broader product and platform roadmap.
• Partner effectively across multiple business areas and levels of leadership to identify, appreciate, and leverage varying perspectives, ways of working, and priorities

• Strong customer focus and insights-driven prioritization. You care deeply about delivering solutions that create real customer value, support business goals, and compound the leverage of our technologies and operations.
• 1-3 years building software, products, or automations in a role where you owned outcomes — not just executed assigned tickets. (Strong new-grad candidates with a demonstrable portfolio of or substantive assignments will be considered for the entry level.)
• Demonstrated ability to build full-stack: you can stand up a working front end and wire it to back-end logic and data.
• Evidence you can run discovery with stakeholders or users — you've talked to real people, understood their need or their problem, and translated it into something you built. Say so explicitly, with the outcome.
• AI-assisted development fluency: you build with AI tools (coding agents, LLM application patterns), not around them. We care that you can learn new AI tooling fast and that you’re staying up-to-date on the latest advancements — far more than which specific tools you've used before.
• Clear written and verbal communication. You can explain a tradeoff to a business leader and a data model to an engineer in the same afternoon.
• Bias toward shipping. You'd rather have a rough working prototype in front of a user this week than a perfect plan next month.

Strongly preferred

• Experience integrating applications with enterprise systems of record via APIs.
• Familiarity with the Microsoft ecosystem (Azure, Azure DevOps, Dynamics/Dataverse, M365) or the ability to ramp into it quickly.
• Exposure to LLM application development — prompt design, retrieval-augmented patterns, agent/tool-use chains, and especially evaluation (building checks that catch hallucinations and regressions before they reach a pilot).
• Comfort working inside messy, real-world business constraints — legacy workflows, fragmented data, security and compliance gates.

Qualifications:

Technical

• Front-end: React, modern JS/TypeScript, responsive UI.
• Back-end & data: API design and integration, SQL, relational data modeling; comfort reading/writing to systems of record under governance.
• AI-assisted build: coding agents and AI development tools; LLM application patterns including prompting, RAG, tool/agent orchestration, and eval design.
• Engineering hygiene: version control and trunk-based development, pull requests and peer review, secrets kept out of source control, SSO/AD-based identity. Familiarity with CI/CD and observability is a plus.
• Our environment is Microsoft-ecosystem aligned, including SSO/AD for auth, Azure DevOps Git for source control, Dynamics Dataverse, and .NET as the production handoff languages.

Product

• Discovery and problem framing: user and stakeholder research, opportunity identification, getting to the real need behind the ask.
• Distillation of complex experiences and workflows, including identification of when to simplify or eliminate elements while maintaining or improving outcomes.
• Opportunity qualification and prioritization against a clear value framework.
• Value modeling: sizing impact credibly enough to defend with finance.
• Outcome ownership: defining success metrics and holding to them after launch.

Human

• Stakeholder empathy and the ability to earn trust quickly inside teams you don't belong to.
• Radical ownership — you treat the problem as yours end to end.
• Adaptability — the tools and AI capabilities will change under you, repeatedly. You stay ahead of that.
• Handoff discipline — you measure success by what reaches production and gets adopted, not by what you personally built.