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Headquarters: Remote URL: https://www.toptal.com/ About the Role We're looking for a Technical Program Manager to lead complex SAP master data transformation programs — spanning S/4HANA migrations, master data governance, and cross-domain data architecture. This isn't a role for someone who manages timelines from the sidelines: you'll need real technical depth in SAP MDG and master data design to make credible calls, sequence cutovers, and serve as the authoritative technical voice across delivery, functional, and platform teams. If you can pair program leadership with hands-on SAP architecture fluency, this role gives you ownership over both the "how" and the "when." What You'll Do Own program-level planning and sequencing for SAP master data initiatives, including migration cutover timing, dependency mapping, and freeze procedures Serve as the authoritative technical advisor across delivery, functional, and platform teams on master data architecture decisions Define and oversee the target-state data model across master data domains (e.g., Customer/Business Partner, Material, Vendor, Finance) Lead design conversations on conversion strategy, hierarchy/classification structures, and golden-record/system-of-record rules Set technical standards for data model extensions, custom fields, and governance workflows (e.g., SAP MDG, BRF+, change request design) Own the mock-load cadence, defect triage process, and reconciliation framework, ensuring data integrity through migration Coordinate cross-functional teams to manage risk, resolve blockers, and keep migration programs on schedule and in scope Provide architectural review for new integrations and downstream distribution patterns touching master data Maintain transparent stakeholder communication, including status reporting, milestone tracking, and translating technical complexity for business audiences What You Bring 10+ years of experience in SAP master data architecture, data architecture, or technical program management within SAP environments Experience with at least one full lifecycle S/4HANA implementation or ECC to S/4 conversion, ideally including Business Partner conversion Hands-on technical depth in SAP MDG or equivalent master data governance tooling Strong understanding of data modeling, hierarchy/classification design, and migration methodology (e.g., SAP Migration Cockpit, LSMW) Familiarity with integration patterns such as ALE/IDoc, DRF/SOA, OData/REST APIs Proven ability to manage program-level risk, scope, budget, and cross-functional coordination Strong written and verbal communication skills, with the ability to translate complex technical concepts for business stakeholders Nice to Have SAP Certified Application Associate — Master Data Governance, S/4HANA Sourcing & Procurement, or Finance PMP or other project/program management certification TOGAF 9 or 10 CDMP (Certified Data Management Professional) Experience with data quality tools such as SAP Information Steward, Syniti, Stibo, Collibra, or Informatica MDM Why This Role Real technical ownership: Lead architecture decisions, not just status meetings High-stakes scope: Own outcomes that determine the integrity of an entire S/4HANA master data landscape Cross-functional influence: Serve as the technical anchor across delivery, functional, and platform teams Depth and breadth: Combine program leadership with deep, hands-on SAP expertise   To apply: https://weworkremotely.com/remote-jobs/toptal-technical-program-manager-sap-master-data-s-4hana-migration-remote

Headquarters: Remote URL: https://www.toptal.com/ About the Role We're looking for an Oracle Integration Cloud Consultant who can own enterprise integration work end to end — discovery, architecture, implementation, migration, and production support. This isn't a "follow the ticket" role: you'll independently design and build production-grade OIC integrations connecting Oracle applications with ecommerce, ERP, CRM, finance, and supply chain platforms. If you know OIC 3 inside and out and want variety across projects and industries, this is built for that. What You'll Do Design and build integrations using Oracle Integration Cloud, including Oracle and third-party adapter configuration Develop scheduled, event-driven, batch, and API-based integrations Create data mappings, transformations, validations, and orchestration workflows Integrate Oracle applications with ecommerce, ERP, CRM, finance, and supply chain platforms Implement error handling, retries, monitoring, logging, and alerting Define data contracts and technical documentation Troubleshoot integration and connectivity issues across environments Support deployments across development, testing, and production Evaluate integration architectures, platform costs, and alternative approaches Collaborate directly with application, infrastructure, and business teams What You Bring Strong hands-on experience with Oracle Integration Cloud, preferably OIC 3 Experience building and deploying production-grade integration flows Experience with at least one Oracle enterprise application (JD Edwards, Oracle ERP Cloud, E-Business Suite, PeopleSoft, or Siebel) Strong knowledge of REST, SOAP, JSON, XML, webhooks, authentication, and data mapping Experience with scheduled, event-driven, synchronous, and asynchronous integration patterns Experience with error handling, monitoring, troubleshooting, and operational support Familiarity with Oracle Cloud Infrastructure Strong communication, documentation, and independent problem-solving skills Nice to Have Experience with JD Edwards adapters or JDE Orchestrator Ecommerce platform integration experience OIC cost modeling or process automation experience Gen2-to-Gen3 migration experience OCI AI services or MCP integration experience Why This Role Variety: Multiple industries and project types — discovery, architecture, migration, and production support Growth path: Potential to contribute as a developer, technical lead, or integration architect depending on project scope Exposure: Enterprise ERP, ecommerce, cloud, and AI-enabled integration work Longevity: Real potential for follow-on engagements beyond the initial project   To apply: https://weworkremotely.com/remote-jobs/toptal-oracle-integration-cloud-oic-consultant

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Headquarters: United States URL: http://lawnstarter.com About LawnStarter LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $100M in annual bookings. We're expanding beyond lawn care to become the one-stop shop for all home services — operating across three brands (LawnStarter, Lawn Love, Home Gnome) on a single shared platform. About Pricing at LawnStarter Upfront pricing is our competitive moat. Most home service marketplaces make you find a Pro and wait for a custom quote. LawnStarter gives customers a price immediately and assigns a Pro. That's a huge differentiator — but it means we have to get pricing right at scale for services where the industry norm is custom quotes for everything. That tension — seamless upfront pricing vs. inherently custom work — is what makes this domain both critical and uniquely challenging. Today, pricing is split across three fragmented systems with no dedicated owner. Requirements The Role You'll own the full pricing and monetization domain — from the infrastructure that powers every price we show to the strategy that determines what we charge, how we bundle, and where we expand. This is a transformation role. We're migrating from three disconnected pricing paradigms to a unified dynamic pricing system. The roadmap has 11 priorities, a dedicated engineering team, and no full-time product owner. What makes this role different: You own the entire pricing domain: Not a slice of pricing alongside other PM work. Pricing infrastructure, dynamic pricing strategy, new revenue models, service availability — all yours. Strong data team partnership: A data team owns pricing models and analytics. You define what to optimize. They figure out how. You don't build models — but you speak the language fluently. Infrastructure-first sequencing: The big pricing gains require a new pricing API and data product first. You need to be the PM who gets energized by building the foundation, not frustrated by it. What You'll Own Dynamic pricing roadmap: Migrate three legacy pricing systems to a unified pricing service. Sequence the 11-priority roadmap, make tradeoff calls, and ship. Pricing strategy: Define pricing for 24+ services across mowing, non-mowing, and emerging verticals. Balance conversion, margin, and Pro economics. New revenue models: Unlock bundles, add-ons (e.g., "bag my clippings"), channel discounts, and frequency-based pricing — none of which exist today. Service availability: Determine where and when we offer services based on supply, demand, and profitability signals. Experimentation framework: Stand up A/B testing for pricing changes, measuring impact on conversion, margin, and Pro claim rates. Problems to Solve Three pricing systems that can't talk to each other. Pre-priced tables (1.8M+ rows of static lookups), instant quote logic (hardcoded per-service rules across APIs), and manual Pro quotes. None can combine location + frequency + brand + supply-demand into one decision. You'll architect the migration to a unified system without breaking pricing that 100K+ customers rely on today. Mowing is 90%+ of revenue and stuck on static pricing. Our biggest service can't apply dynamic variables like supply tightness, seasonal demand, or channel discounts. A $3 price change swings conversion by ~10%. You'll partner with the data team to build pricing intelligence into mowing without destabilizing the core business. 24+ services need to migrate, each one different. Bush trimming, pool cleaning, landscaping, leaf removal — different pricing variables, ordering flows, customer expectations. You'll define the migration sequence and determine which services get dynamic pricing vs. simplified models. No bundles or add-ons exist. Customers can't bundle services for a discount or add options to their mowing — a major untapped revenue and retention opportunity. You'll design the pricing architecture that makes bundling possible. What Success Looks Like (Year 1) Pricing API/service shipped and live — Replaces at least one legacy paradigm with a clear path to consolidating the others Mowing on dynamic pricing — Core service running on the new system with measurable margin or conversion impact Experimentation framework operational — Team can A/B test pricing changes and measure impact within days, not months Bundle/add-on architecture defined — System design complete and engineering building, even if not yet live Who You Are AI-native. You use AI daily — scenario modeling, pricing analysis, data exploration, drafting specs. You push AI into parts of your workflow others haven't thought of yet. This is unlikely to be a good fit if you view AI as a novelty rather than a core productivity lever. A systems thinker who architects platforms, not just sets prices. You see pricing as a system: inputs, rules, feedback loops, edge cases. You can design a pricing architecture that handles 24 services across 3 brands with different economics — and explain it to an engineer in a way they can build. This is unlikely to be a good fit if your pricing experience is limited to spreadsheets or optimizing a single product's price point. Data-informed, not data-dependent. You partner with the data team to define what to optimize and interpret results. You know when the data is insufficient and a judgment call is needed. This is unlikely to be a good fit if you either ignore data or refuse to move without perfect information. Technically fluent. You work directly with engineers on API design, discuss schema tradeoffs, and review technical design docs with real feedback. You don't write code, but you earn engineering's trust by speaking their language. This is unlikely to be a good fit if you treat engineering as a black box or need everything translated into business terms. A patient builder. The pricing gains require infrastructure first — a pricing API, a data product, migration tooling. You're energized by building the foundation, not frustrated that the payoff isn't immediate. This is unlikely to be a good fit if you need quick wins to stay motivated or lose interest when the work is foundational. Monetization-minded. You see bundles, add-ons, service availability, and frequency pricing as revenue levers, not just features. You naturally think about margin, willingness to pay, and Pro economics. This is unlikely to be a good fit if you think of pricing as a one-time decision rather than an ongoing optimization problem. This Role Is NOT A quick-wins role. Massive technical debt (1.8M-row pricing tables, hardcoded logic across APIs). The pricing API and data product must be built before dynamic pricing gains materialize. If you need visible impact in 90 days, this will be frustrating. A solo act. Every pricing change touches engineering, data, sales, and support. If you prefer autonomous execution with minimal coordination, the dependency load here will feel heavy. A data science role. You partner closely with the data team, but you're not building pricing models. If you want to spend your days in notebooks running regressions, this isn't the right fit. An optimization-only role. You're building a pricing system from scratch while keeping the current ones running. If you prefer optimizing within an established framework, the ambiguity here will be uncomfortable. Benefits Compensation & Benefits Base salary: $140,000 - $185,000 Equity: Pricing decisions directly impact revenue, margin, and conversion at scale. We want you invested in the long-term outcome of the system you're building. Healthcare: Medical, dental, and vision Fully remote: Pricing work requires deep analysis and focused thinking. We trust you to manage your environment. Flexible PTO: We focus on results. Take what you need. LawnStarter provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. We comply with applicable state and local laws governing nondiscrimination in employment. To apply: https://weworkremotely.com/remote-jobs/lawnstarter-senior-product-manager-pricing

Headquarters: United States URL: http://lawnstarter.com About LawnStarter LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $100M in annual bookings. We're expanding beyond lawn care to become the one-stop shop for all home services — operating across three brands (LawnStarter, Lawn Love, Home Gnome) on a single shared platform. About Service Delivery at LawnStarter We make a promise on both sides of the marketplace: customers get a job done well, and Pros get paid fairly for doing it. Service delivery is everything that happens after a customer books — keeping that promise through completion, support, and the moments when things don't go to plan. This is the hard part of running a marketplace: we broker work we can't directly see, between two parties whose interests sometimes collide. Get it right and people stay for years. Get it wrong and you lose customers, Pros, or both. Trust is the product — and increasingly, the systems that protect it are AI-powered. Requirements The Role This is a broad Senior PM role on the quality, trust, and communication side of service delivery — setting the right expectations on both sides, steering Pros to deliver great work, resolving conflicting interests fairly, and making our AI-powered support genuinely good. You'll work on live, high-scale systems with a mandate to make them better. Service delivery is a big area with more than one PM in it. You'll work alongside them; your center of gravity is the trust between both sides of the marketplace. What makes this role different: It's a real area of impact, not a single feature. You'll shape strategy, policy, and the systems behind a whole slice of the post-booking experience — not optimize one screen. It's inherently two-sided. You work for customers and Pros at once, and you're often the one arbitrating between them. Pleasing one side at the other's expense is failure. AI is a core tool, not a side project. Support and outreach already run on AI. You'll push how far that goes — and build the evals that prove it's good before we trust it with more. Problems to Solve Getting expectations right before the work ever starts. Most service failures aren't bad work — they're mismatched expectations. The grass grew a tier past what was booked; "deep clean" meant something different to each side; the yard didn't match the photos. You'll make sure what's promised to the Pro matches what the customer actually expects, that those expectations are the right ones for the property and service, and that Pros are steered toward delivering great work. Every mismatch you prevent up front is a conflict you never have to resolve later. Arbitrating genuinely competing interests. A customer wants a date the Pro can't commit to. The grass is long enough to need a different price tier than booked. The job wasn't what the listing photos suggested. These aren't bad actors — both sides are right from where they sit, and the marketplace has to make a call. You'll build the policies and the (increasingly AI-assisted) decision systems that resolve these fairly and consistently at scale — and make the judgment call yourself when there's no clean answer. Making AI trustworthy enough to do more — which means evals. Customer-side AI already handles ~34% of support at roughly a penny per message, and Pro-side support is still to be built. Expanding either is gated on one hard, ongoing, technical thing: can we prove the AI resolved a case rather than just closed it? You'll own the eval systems — golden datasets, automated judges, regression detection, human-review sampling — that define what "good" means for an open-ended conversation, gate every change, and catch quality drift before a customer feels it. This is the deep, unglamorous work that makes a slick demo safe to scale. Messaging that connects both sides — and quietly protects the marketplace. The inbox is how 500K+ customers and 20K+ Pros coordinate across all three brands — and the record we lean on when something goes sideways. It also has to moderate: catching when a relationship is drifting off-platform (disintermediation) or a conversation is heating into conflict. You'll keep communication easy for the legitimate 99% while spotting the patterns that quietly cost us customers, Pros, and revenue. What Success Looks Like (Year 1) Expectations match on both sides: Pros know exactly what each job requires, customers get what they expected, and fewer jobs go sideways from misalignment in the first place. Competing interests resolve faster and more fairly: A clearer resolution model with measurable consistency — and more of these conflicts prevented up front by better expectation-setting. AI is provably good, and does more: An eval suite gates AI changes so no quality regression ships unseen — and on that foundation, higher resolution on customer support plus a first version of Pro-side AI support live. Messaging connects and protects: Measurable improvement in messaging reliability and engagement, plus working moderation that catches off-platform leakage and conflict early. Who You Are AI-native. You use AI daily in your own work, and you have real intuition for how to measure whether an AI experience is actually good — not just whether it shipped. You've built or owned evals, or you're hungry to, because you know that's what separates a trustworthy agent from a demo. This is unlikely to be a good fit if you treat LLM quality as a vibe check or as engineering's problem to figure out. Comfortable making two-sided calls. You can hold both the customer's and the Pro's interest in your head at once, and you're willing to make the call when they conflict — clearly, and with a rationale you'd defend to either side. This is unlikely to be a good fit if you're a people-pleaser who can't say no, or if you instinctively optimize for one side and forget the other exists. You design for the right outcome up front. You're not satisfied grading work after the fact — you'd rather get the expectations right at the start so the job goes well in the first place, for both the customer and the Pro. This is unlikely to be a good fit if you gravitate to measuring and auditing results over shaping them before they happen. A marketplace systems thinker. You see service delivery as a system of incentives, policies, and feedback loops — not a set of screens — and you design rules that hold up across edge cases and bad actors. This is unlikely to be a good fit if your instinct is to solve every problem with UI rather than incentives and policy. Data-informed. You live in the numbers that matter here — CSAT, resolution rate, eval scores, churn, deflection — and you know when the data is thin enough that a judgment call is needed. This is unlikely to be a good fit if you either ignore data or refuse to move without perfect information. Technically fluent. You partner with engineers on how AI and messaging systems work and give real feedback on design tradeoffs. You don't write production code, but you don't treat the systems as a black box either. This is unlikely to be a good fit if you need everything translated out of technical terms first. This Role Is NOT A support PM role. Support is one surface of a much broader product area. If you picture your days as managing a ticket queue or a help center, this isn't that. The internal-AI role. We have a separate PM driving AI adoption in internal tooling. This role is about AI in the user-facing service-delivery experience — customers and Pros, not internal teams. A greenfield 0→1 role. These systems are live and serving hundreds of thousands of people today. You'll improve and re-architect under real load, not build from a blank page. A single-audience role. You serve — and arbitrate between — both customers and Pros. If you only want to think about one side of the marketplace, the tension here will be uncomfortable. Benefits Base salary: $140,000 - $185,000 Equity: The systems you'll work on touch every customer and Pro — quality, trust, and retention all run through service delivery. We want you invested in the long-term outcome. Healthcare: Medical, dental, and vision Fully remote: Work from anywhere in the US. This role requires deep focus and close partnership with engineering and ops — we trust you to manage your environment. Flexible PTO: We Focus On Results LawnStarter provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. We comply with applicable state and local laws governing nondiscrimination in employment. To apply: https://weworkremotely.com/remote-jobs/lawnstarter-senior-product-manager-service-delivery

Who We Are: HelioCampus was founded with the purpose of helping college & university leaders navigate today’s growing pressures.

About the JobCrawford Hoying is dedicated to creating exceptional communities, delivering outstanding service, and driving long-term value through innovation and operational excellence.