LawnStarter

• You'll be the first person at LawnStarter dedicated to data governance - the owner of whether our data can be trusted. • That means the quality and freshness of our source data, pipelines, and reports; the definitions behind our metrics; the standards behind our Segment event tracking; the health of our Lightdash workspace; the data feeding our machine learning models; and the security of the data itself. • This is a hands-on role. You'll work solo at first, with the Analytics team around you but nobody under you - building automation, writing checks, fixing what's broken, and putting processes in place that scale past you. If the scope grows the way we expect, this becomes the foundation of a team you'd build. • Data quality and freshness - automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream; running incidents to resolution when they happen. • Data lineage and impact analysis - a living map from production source to warehouse model to dashboard, and the process that uses it: when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships, not discovered after. The end-state is data contracts with engineering, so breaking changes get caught in their workflow, not ours. • Lightdash - administration, workspace structure, permissions, and the rollout itself. Your job is to give the company self-serve autonomy while keeping the workspace tidy enough that people can find and trust what's there. Enablement is part of the deal - people follow standards they've been taught - and so is keeping queries fast and warehouse costs sane. • The semantic layer - we just shipped it for our most critical metrics: one governed definition per metric, in code. You'll extend definition and mapping to the rest and guard the layer against uncontrolled growth as it scales. • Event tracking governance - our governed Segment event catalog: reviewing new events against its standards, keeping it matched to what production actually sends, and evolving the guardrails (naming, property dictionary, drift detection) as tracking grows. • AI data readiness - AI agents query our warehouse every day through Brain, our internal AI toolkit. You'll govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for an agent to query and get the right answer. • Data security and privacy - access controls, PII handling and retention under US state privacy laws, and periodic reviews of who - and which AI tools - can see what. • The governance system itself - the documentation, ownership models, and review loops that keep all of the above running without heroics.

Role Description You'll be the first person at LawnStarter dedicated to data governance - the owner of whether our data can be trusted. That means the quality and freshness of our source data, pipelines, and reports; the definitions behind our metrics; the standards behind our Segment event tracking; the health of our Lightdash workspace; the data feeding our machine learning models; and the security of the data itself. This is a hands-on role. You'll work solo at first, with the Analytics team around you but nobody under you - building automation, writing checks, fixing what's broken, and putting processes in place that scale past you. If the scope grows the way we expect, this becomes the foundation of a team you'd build. What makes this role different: - You're first. Governance has been everyone's side job, so what exists today is yours to reshape - keep what works, redesign what doesn't, and your standards become the company's standards. - Whole-stack ownership. Source data to pipelines to dashboards and ML models - you own trust across the entire chain, not one slice of it. - A live migration to shape. Lightdash is landing now. You get to set up its permissions, structure, and norms before bad habits form, instead of untangling them later. What You'll Own: - Data quality and freshness - automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream; running incidents to resolution when they happen. - Data lineage and impact analysis - a living map from production source to warehouse model to dashboard, and the process that uses it: when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships, not discovered after. - Lightdash - administration, workspace structure, permissions, and the rollout itself. Your job is to give the company self-serve autonomy while keeping the workspace tidy enough that people can find and trust what's there. - The semantic layer - we just shipped it for our most critical metrics: one governed definition per metric, in code. You'll extend definition and mapping to the rest and guard the layer against uncontrolled growth as it scales. - Event tracking governance - our governed Segment event catalog: reviewing new events against its standards, keeping it matched to what production actually sends, and evolving the guardrails (naming, property dictionary, drift detection) as tracking grows. - AI data readiness - AI agents query our warehouse every day through Brain, our internal AI toolkit. You'll govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for an agent to query and get the right answer. - Data security and privacy - access controls, PII handling and retention under US state privacy laws, and periodic reviews of who - and which AI tools - can see what. - The governance system itself - the documentation, ownership models, and review loops that keep all of the above running without heroics. Qualifications - Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy. - AI-native. You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go. - A hands-on senior operator. You write the SQL, debug the Airflow DAG, and configure the permissions yourself. - Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. - An enforcer people actually like. You'll hold engineers and analysts you don't manage to standards. Requirements - Zero pipeline incidents from unannounced source-data changes. - Zero freshness incidents - stakeholders never open a stale dashboard. - Every area of the business manages on official, well-maintained metrics and dashboards. - Every Segment event has an owner and a standard. - Governance runs as a system - documented processes that would survive you taking a month off. Benefits - Base salary: $75k–$100k/year - Equity: The whole company makes decisions on the data you'll guard. - Fully remote: This work needs deep focus, and we trust you to manage your environment. - Flexible PTO: We focus on results. Take what you need.

• 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.

Role Description 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. 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. Partner with the data team to build pricing intelligence into mowing without destabilizing the core business. - 24+ services need to migrate, each one different. Define the migration sequence and determine which services get dynamic pricing vs. simplified models. - No bundles or add-ons exist. 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. Qualifications - AI-native: You use AI daily — scenario modeling, pricing analysis, data exploration, drafting specs. - A systems thinker who architects platforms, not just sets prices. - Data-informed, not data-dependent: You partner with the data team to define what to optimize and interpret results. - Technically fluent: You work directly with engineers on API design and discuss schema tradeoffs. - A patient builder: The pricing gains require infrastructure first — a pricing API, a data product, migration tooling. - Monetization-minded: You see bundles, add-ons, service availability, and frequency pricing as revenue levers. Requirements - This Role Is NOT: - A quick-wins role: Massive technical debt must be addressed before dynamic pricing gains materialize. - A solo act: Every pricing change touches engineering, data, sales, and support. - A data science role: You're not building pricing models. - An optimization-only role: You're building a pricing system from scratch while keeping the current ones running. Compensation & Benefits - Base salary: $140,000 – $175,000 - Equity: Pricing decisions directly impact revenue, margin, and conversion at scale. - Healthcare: Medical, dental, and vision - Fully remote: Pricing work requires deep analysis and focused thinking. - Flexible PTO: We focus on results. Take what you need.

• Automated test coverage: Design and grow our E2E and integration test suite using Cypress. Set the strategy, prioritize coverage gaps, and raise the floor across the platform. • Bug process: Own the full lifecycle from triage to production fix. Keep it moving, keep stakeholders informed, and make sure nothing falls through the cracks. • Cross-functional quality partnership: Collaborate with PMs, Designers, and the Data team to review requirements, raise risks early, and define what "done" actually means. • Process improvement: Identify weaknesses in how we deliver software and propose solutions. • Exploratory testing: Test new features and existing flows on web and mobile across devices, catching what automation misses.

Role Description As we scale into new service verticals and grow the engineering team, quality can't be reactive. This role exists to make quality proactive: owning the automation coverage, shaping the bug process, and partnering closely with Product, Design, and the Data team to catch problems before they reach customers. You'll report to the Engineering Manager and be embedded across the engineering organization. This is hands-on and strategic, helping raise the quality ceiling for the whole team. What makes this role different: - Cross-functional influence: Work directly with engineers, PMs, Designers, and the Data team to surface risks early, understand requirements deeply, and propose better solutions. - Automation ownership: Own the strategy for building and expanding automated test coverage. - Process shaper: Identify gaps in the quality process and have the authority to fix them. What You'll Own: - Automated test coverage: Design and grow our E2E and integration test suite using Cypress. - Bug process: Own the full lifecycle from triage to production fix. - Cross-functional quality partnership: Collaborate with PMs, Designers, and the Data team to review requirements and define what "done" means. - Process improvement: Identify weaknesses in how we deliver software and propose solutions. - Exploratory testing: Test new features and existing flows on web and mobile across devices. Problems to Solve: - Keeping automation coverage sharp as the product evolves. - Exploring new tools and leveraging AI to improve quality operations. What Success Looks Like (Year 1): - Automation coverage stays strong with new features shipping with automated tests. - Bug process runs smoothly with a predictable triage to fix cycle. - Deep business knowledge to anticipate edge cases. - Quality process improvement shipped with at least one meaningful improvement in place. Qualifications - AI-native: Use AI tools like Claude Code as a standard part of your work. - Automation-first: Default to asking how to prevent problems automatically. - Customer-obsessed: Think like a user when testing flows. - Adaptable: Comfortable with rapid evolution in tools and processes. - Collaborative and direct: Work across teams and surface risks early. - Technically solid: Comfortable reading and writing code across the stack. Requirements - This role is NOT a manual-testing role. - Not a ticket-executor role; expected to shape work. - Not a single-tool role; adaptability is key. - Comfortable with ambiguity in a fast-changing environment. - Quality is a first-class concern; expected to influence processes. Benefits - Base salary: $45-$65k - Equity: Invested in the quality and long-term health of the platform. - Fully remote: Work from anywhere in the Americas. - Unlimited PTO: Focus on results and take what you need.

• You are the team's dedicated sourcer. You don't own requisitions end-to-end — you own the top of the funnel for every search that needs outbound talent. • Your job is to find, engage, and deliver qualified candidates to the recruiters who manage each role. • You'll work most closely with the Director of Talent Acquisition and two Senior Recruiters. • You'll source for US-based roles (Growth, Product, Design, Executive) and LATAM-based engineering roles. • Every role is different — you might be mapping Directors of Product one week and sourcing Staff Engineers in Brazil the next. • Outbound sourcing across all active roles: Build candidate pipelines using LinkedIn Recruiter, Boolean search, Talent Insights, and any other channels that produce results. • For each role, define the sourcing approach — target companies, titles, geographies, keywords, and outreach messaging. Adapt as you learn what's working and what's not. • Write and send outreach that gets responses. Manage multi-touch sequences. Track response rates and iterate on messaging. • Own your numbers — outreach volume, response rates, qualified candidates delivered, source-to-screen conversion. Report weekly. • Join kickoff calls with hiring managers to hear the brief firsthand. You source better when you understand the role directly, not through a game of telephone.

Role Description You are the team's dedicated sourcer. You don't own requisitions end-to-end — you own the top of the funnel for every search that needs outbound talent. Your job is to find, engage, and deliver qualified candidates to the recruiters who manage each role. Today, our two LinkedIn Recruiter seat holders split their time between sourcing, screening, pipeline management, and closing. That context-switching slows everything down. You exist to eliminate that bottleneck — one person, full-time in LinkedIn, accountable to sourcing metrics across all active roles. You'll work most closely with the Director of Talent Acquisition and two Senior Recruiters. You'll source for US-based roles (Growth, Product, Design, Executive) and LATAM-based engineering roles. Every role is different — you might be mapping Directors of Product one week and sourcing Staff Engineers in Brazil the next. What makes this role different: - Pure sourcing, not full-cycle: You don't screen, you don't manage pipelines, you don't negotiate offers. You source. That's the entire job, and you'll be measured on it. - Cross-functional breadth: You'll source across every function we hire for — marketing, product, engineering, design, executive. You need range, not just one vertical. - US hiring managers, PH-based team: You'll join sourcing intake calls with US-based hiring managers to understand what they're looking for. You need to be comfortable in those conversations — asking calibration questions, pushing back on unrealistic profiles, and translating requirements into search strategies. - Small team, high ownership: There's no sourcing manager between you and the work. You set your own search strategies, manage your own pipeline, and report on your own numbers. What You'll Own: - Outbound sourcing across all active roles: Build candidate pipelines using LinkedIn Recruiter, Boolean search, Talent Insights, and any other channels that produce results. You'll typically support 4-6 active searches simultaneously. - Search strategy: For each role, define the sourcing approach — target companies, titles, geographies, keywords, and outreach messaging. Adapt as you learn what's working and what's not. - Candidate engagement: Write and send outreach that gets responses. Manage multi-touch sequences. Track response rates and iterate on messaging. - Sourcing metrics and reporting: Own your numbers — outreach volume, response rates, qualified candidates delivered, source-to-screen conversion. Report weekly. - Market intelligence: When a search is tough, tell us why — is the talent pool too small? Is comp off-market? Are we targeting the wrong profile? Bring data, not just opinions. - Sourcing intake participation: Join kickoff calls with hiring managers to hear the brief firsthand. You source better when you understand the role directly, not through a game of telephone. Problems to Solve: - Our recruiters are splitting time between sourcing and closing — and both suffer. - Only 2 team members have LinkedIn Recruiter seats, and they're also managing pipelines, running screens, and closing candidates. When they're in back-to-back screening calls, sourcing stops. When they're deep in a sourcing sprint, candidates in-process don't get moved. You break that trade-off by owning sourcing full-time. - Director-level and technical roles require proactive sourcing — inbound isn't enough. - For roles like Director of Product Management, Director of SEO, and Staff Product Engineer, the best candidates aren't applying. They need to be found, engaged, and convinced. Our inbound pipeline is strong for volume roles, but senior and technical searches live or die on outbound quality. - We need sourcing accountability with dedicated metrics. - Today, sourcing happens alongside everything else, so it's hard to measure what's working. We need one person whose job is sourcing, with clear metrics: outreach volume, response rates, qualified candidates delivered per role, and source-to-screen conversion. What Success Looks Like (First 90 Days): - You've sourced qualified candidates for every active role — not just volume, but candidates who pass screening and advance to WHO interviews. - Response rates on outreach are above 15% — your messaging is personalized, relevant, and gets replies. - Recruiters are spending more time closing and less time sourcing — the pipeline is flowing without them having to hunt. - You've built reusable search strategies — target company lists, Boolean strings, and outreach templates that the team can reference and build on. - You've identified at least one sourcing channel or approach we weren't using — whether that's a new platform, a different search methodology, or a creative way to reach passive talent. Qualifications - A LinkedIn Recruiter power user. - Experienced sourcing US-market roles. - Comfortable across functions. - Metrics-driven. - A strong written communicator. - AI-forward. - Self-directed. Requirements - 5+ years of sourcing or recruiting experience, with at least 3 years sourcing US-market roles. - Hands-on LinkedIn Recruiter experience (not just LinkedIn — the Recruiter product specifically). - Demonstrated experience sourcing senior-level roles (Director, VP, Staff/Principal Engineer, or equivalent). - Experience sourcing across multiple functions (not single-vertical only). - Strong written English — outreach quality is a core part of this role. - Comfortable working US business hours overlap (minimum 4-5 hours of CT overlap daily). Nice to Have - Experience with Workable ATS. - Experience sourcing LATAM engineering talent. - Familiarity with AI sourcing tools (SeekOut, hireEZ, or similar). - Experience in a marketplace, tech startup, or high-growth environment. Benefits - Competitive base salary: PHP 95K - 130K, depending on experience (full-time contractual). - Fully remote: Work from anywhere in the Philippines. - LinkedIn Recruiter seat: You'll have your own seat — this is non-negotiable for the role. - 13th month pay. - Flexible PTO: We focus on results. Take what you need.

• 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. • You'll work alongside other PMs; your center of gravity is the trust between both sides of the marketplace. • You'll shape strategy, policy, and the systems behind a whole slice of the post-booking experience — not optimize one screen. • You'll build the policies and the decision systems that resolve these fairly and consistently at scale — and make the judgment call yourself when there's no clean answer. • 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.

• The PM team: Lead, coach, and multiply three already-strong PMs — and hire more as we grow. • Prioritization: Own the growth/retention/profitability tradeoffs on one shared roadmap — what gets built, what waits, what we kill. • Multi-service strategy: Turn one proven vertical into a repeatable playbook across services, designing for customers and Pros as one system. • Agent-native direction: Own how AI and agents show up in what we build — for our team, our customers, and our Pros. • Cross-functional partnership: Be the product leader the rest of the company trusts to make the right calls.