Data & Platform Product Manager

Job Description
WorldStrides moves hundreds of thousands of students, families, teachers, and schools through complex financial transactions every year — and generates a mountain of operational and behavioral data along the way. Two problem spaces sit under-invested in from a product perspective: how families and institutions pay for and finance educational travel, and how the company sees, trusts, and acts on its own data. Both directly shape customer trust, business economics, and the speed at which the rest of the product organization can move.

We are actively migrating our data platform into Microsoft Fabric, with Power BI as our analytics and reporting surface — a foundational shift that this role will help shape from the product side. This is a builder's role. You will own two adjacent problem spaces — Platform and Data — partner with pods in each, and be measured on the outcomes your work moves, not on the volume of features you ship.

How we build product
This role is anchored to a specific view of how product gets built going forward. If this is how you already want to work, you will thrive here.
• Discovery is the moat. AI makes generation cheap. The bottleneck is no longer "what do we build" but "are we solving the right problem." Sharp problem framing beats ten solutions chasing a fuzzy one.
• Portfolios over roadmaps. We run broad, lightweight experiments in parallel and double down on what's working. We do not defend a 12-month roadmap; we defend a thesis and a learning velocity.
• Prioritize attention, not resources. Human judgment on what's worth testing and what the results mean is the scarce resource. We spend it deliberately.
• Generate fast, judge slow. Use AI to produce many candidate problem frames, concepts, and prototypes quickly. Apply rigorous, deliberate validation and strategic bets before scaling.
• Smaller teams, bigger ownership. Autonomous pods own full problem spaces end-to-end. PMs are accountable for outcomes, not feature counts.
• Raise the quality bar. Speed-to-generate must not substitute for depth-of-value. We tighten outcome thresholds, not loosen them.
• Strategic sequencing. Cheap experiments must compound into defensible architecture, not pile up as disconnected features.

What you will own
• Two adjacent problem spaces, sized so one thoughtful PM can hold both without either getting neglected: Platform (payments, payment plans, refunds and chargebacks, group billing, financial aid, third-party financial partnerships like trip protection and financing) and Data (data quality, pipelines that feed reporting and personalization, self-serve analytics for internal teams, instrumentation the experimentation program depends on).
• Product ownership of our Microsoft Fabric migration and Power BI surface — helping define what "good" looks like for internal data consumers, what use cases the platform must serve, and how we prioritize the migration so it delivers value along the way rather than only at the end.
• The full discovery-to-outcome loop across both spaces: who the customer is (families, schools, internal finance, internal data consumers), what is broken, what is worth solving, what we will try, what we learn, what we double down on.
• Day-to-day partnership with pods in each area — dedicated engineers and shared collaborators (design, research, data engineering, finance business partners, compliance) whose time you will share with other PMs. Earning their best work is part of the job.
• Cross-space sequencing. Platform outcomes often depend on the data foundation being right first — you will know when that's true and when it isn't.
• A portfolio of live experiments across both spaces, with explicit hypotheses, success thresholds, and kill criteria.
• Quarterly outcome targets tied to money and trust — payment success, time-to-first-payment, chargeback rate, data self-service response time, data quality SLAs — not output volume.

What you will do
• Frame problems sharply across two lanes. Synthesize customer research, transactional data, finance conversations, and internal user interviews into problem statements precise enough that the team can disagree productively. Toggle context between spaces without losing depth in either.
• Treat internal users like customers. Data platform work has customers inside the company — analysts, ops teams, PMs, marketing, finance. Understand their jobs, prototype your solutions in Power BI or lightweight tools, measure outcomes the same way you would for an external user.
• Shepherd the Fabric migration from the product side. Partner with data engineering on the migration to Microsoft Fabric — sequencing which domains and use cases move first, defining semantic models and Power BI surfaces that internal teams will actually adopt, and making sure the migration ships incremental wins rather than a big-bang cutover.
• Design experiments. Translate problem frames into the smallest tests that would change your mind. Default to many small bets over one big one — even in finance and data, where the instinct is to plan large.
• Use AI as a force multiplier. Draft schemas, generate SQL and DAX, summarize financial policy documents, synthesize interview transcripts, prototype internal tools, and pressure-test your own thinking. You don't need to have shipped AI features; you do need to actively use AI in how you work.
• Judge slowly and deliberately. Set explicit outcome thresholds before tests run. Be honest about signal vs. noise. Kill ideas — including your own — that don't clear the bar.
• Sequence strategically across spaces. Ensure each experiment compounds: financial infrastructure and data assets that future bets can build on, not one-off wins.
• Navigate regulatory and compliance edges. PCI, financial reporting, data governance, PII handling. You don't need to be a lawyer, but you know when to bring one in — and you build controls into product decisions early, not bolted on later.
• Communicate up and across. Tell leadership, your pods, finance, data, and compliance a clear, honest story about what we are learning, where we are betting, and where we are stuck.
• Make shared resources want to work with you. Designers, engineers, and business partners split their attention across pods. You earn their best thinking by bringing sharp problems, respecting their craft, and being a generous teammate — not by competing for their time.

What we are looking for
• 5+ years in product management, ideally including at least one of: payments or fintech, data or analytics platforms, financial operations product (billing, subscriptions, revenue), internal tooling, or B2B SaaS with a technical customer.
• Comfort in structured, quantitative problems. SQL and spreadsheet fluency; can read a financial model without flinching; can talk with a data engineer about a pipeline or semantic model design and hold your end of the conversation.
• Working familiarity with Power BI as a consumer at minimum — enough to know what a good report looks like, what a bad one looks like, and where the seams are.
• Strong discovery instincts. You have done your own customer research, whether that customer is a parent, a finance analyst, or a data engineer.
• A track record of moving customer or business outcomes — not just shipping. You can point to bets you killed and explain why.
• Comfort with regulatory and compliance work — PCI, financial policy, data governance, PII — as part of product decisions rather than a downstream tax.
• Comfort holding a portfolio of unfinished bets across two spaces at once without forcing premature closure.
• Active, curious use of AI tools in your daily work — for schema and SQL generation, research synthesis, writing, analysis, or anything else. You don't have to be an AI expert; you do have to be the kind of person who would have tried five tools by next month.
• Crisp written communication. You can write a one-pager that changes minds — for a finance leader, a data leader, or a customer.
• Comfort with ambiguity and a small team. This is a focused structure, not a layered org.

Nice to have
• Direct experience shipping payments, billing, or subscription products.
• Direct experience as PM for a data platform, analytics team, or experimentation platform.
• Hands-on experience with Microsoft Fabric — OneLake, Data Factory pipelines, lakehouses, semantic models — either as a builder or as a PM.
• Deeper Power BI fluency — semantic models, DAX literacy, workspaces and deployment pipelines, row-level security, adoption strategies.
• Experience running or leading a data platform migration, especially into a modern lakehouse / medallion architecture.
• Experience in a two-sided or three-sided model where financial operations touch multiple personas (e.g., families, schools, internal ops).
• Familiarity with common tooling in these spaces.
• Experience partnering closely with finance or accounting business units on product decisions.
• EdTech or travel background, especially where financial transactions are group-based (schools, teams, tours) as well as individual.

How we will know you are succeeding
• The problems your pods choose to work on are sharper over time — better framed, more clearly worth solving — in both spaces.
• Your pods run a healthy cadence of experiments, and a strong share of them produce clear yes/no answers we can act on.
• Financial outcomes that matter — payment success, first-attempt approval rate, chargeback rate, financial trust signals — move on a small number of metrics.
• Data outcomes that matter — self-service response time, data quality, Power BI adoption, instrumentation coverage for the rest of the product org — move on a small number of metrics.
• The Fabric migration is delivering usable value in increments, not stalled in a multi-year replatform.
• The rest of the product organization can move faster because the data foundation you are building supports them.
• Finance, data, and compliance stakeholders trust the work enough to move at your speed.
• Collaborators across the company want to work with you, and say so.

Work Perks:

• Comprehensive health benefits: medical, dental, vision, life, AD&D, accident, critical illness, disability insurance, FSA/HSA options with employer HSA contributions, and a generous 401(k) match.
• Generous paid time off including 11 paid company holidays, 25 PTO days the first year (prorated), 1 paid volunteer day, 4 mental health days, and bereavement
• Professional development support, including up to $5,250 annually for tuition reimbursement and up to $1,000 for certifications annually
• Opportunities for paid and discounted travel
• Flexible work schedule providing on-site, remote, and hybrid office opportunities
• Encouraged participation in a wide variety of Employee Resource Groups
• Employee Assistance Program (EAP) and wellness resources, including fitness centers at select locations
• Paid parental, caregiver, and disability leave
• Team Member Discount Program
• Engaging, collaborative, and fun work environment