Position: Senior Power Platform Developer – Innovate, Automate, and Elevate In today’s digital landscape, data isn’t just an asset—it’s the key to operational excellence.

About BJAKThe original mission of BJAK is we believe people deserve smarter ways to plan, save and grow their money.

Work Arrangement• Employment Type: Full-Time • Location: Remote • Schedule: Full-Time with required overlap with US business hours • Time Zone Requirement: Pacific Time overlap required About the RoleOur Client is looking for a Backend Developer to join our Engineering team full-time and take ownership of our lead-capture ecosystem.

Job overview Find the right publications, blogs, newsletters, social media personalities, and collaborators that fit our messaging and reach our audience, then run outreach to land guest posts, features, backlinks, and partnerships.

At IT Labs, we work with premier global consulting organizations to help Tier 1 banks and insurance companies transform operations through AI, automation, and advanced analytics, with a special focus on Anti-Financial Crime.

Headquarters: California URL: https://imerit.ai/ iMerit is looking for detail oriented analysts to evaluate and rank AI generated responses to image based prompts. You will judge answers on accuracy, relevance, clarity, conciseness, safety, localization, and how well they follow the user's instructions, then explain your reasoning in writing.Much of the job comes down to this: look at the image, look at what the model said about it, and decide whether the two actually match. What you will do Interpret conversational context and identify what the user really wanted Rate and rank responses against defined quality criteria Compare multiple answers and explain in writing why one wins Verify factual claims using approved research sources Flag tasks that cannot be reliably assessed rather than guessing What you bring: Strong critical thinking and sound judgment in ambiguous cases Solid research skills and attention to detail Excellent reading comprehension Self direction and the discipline to hit deadlines without supervision Good to know Independent contractor engagement for the length of the project.  Fully remote and flexible. You choose your hours as long as volume and deadlines are met. Task volume varies with project demand.  To apply: https://weworkremotely.com/remote-jobs/imerit-ai-response-evaluator

Headquarters: Rochester, New York URL: https://www.assignr.com Come Join Our Team! We strive to provide outstanding support to our customers, and we are looking for a team member to help us out. Most of our customer support is provided via email and Intercom messenger, with some support by phone and via Zoom web conference. As a member of the Assignr support team, you will: Respond to and handle tech support tickets, including resetting passwords, helping customers navigate and use the system, and scheduling/conducting short Zoom walk-throughs for more challenging support questions. Develop the technical support knowledge base of our software platform, including writing help documents and creating how-to video tutorials. Answer incoming customer phone calls to listen to and resolve issues, answer questions, and capture feedback for how Assignr can improve the user experience. Troubleshoot technical issues within the system and solve problems as they arise. Requirements Autonomy: We’re looking for someone who can work independently with minimal guidance or oversight. Attention to Detail: You are thorough and accurate when reading, interpreting and performing tasks. Client Relations/Customer Service: You enjoy providing great service to our customers. Communication: You are fluent in English, and can effectively listen and share knowledge and information with others. You communicate effectively, both in written and oral forms, using proper spelling and grammar. Most support is provided in writing (Intercom messenger and email), so it is very important that you are skilled in written communication with our customers. Problem-Solving Skills: You use critical thinking skills to work through the details of a problem to reach a solution. Resourcefulness: You can creatively handle new situations or difficulties skillfully and promptly. Taking Initiative + Self-Driven: You can see an opportunity or need and act upon it without being asked or told. You like making things happen, rather than waiting for something to happen. Working Hours This is a full time position. We expect the workload to be approximately 35-40 hours per week. Must be available to work primarily during business hours in the Pacific time zone (e.g. Monday - Friday 9a-5p Pacific time), with occasional coverage on Saturdays. Must be authorized to work in the United States. Benefits USD $27/hour Health insurance (medical, dental, vision) Participation in retirement plan with company match Fully remote position Required Education and Experience Minimum formal education of high school diploma or GED. Intermediate computer knowledge a must, including the use of web applications in general, and understanding web technology from an end-user’s perspective. Ability to independently learn and implement new technologies quickly. Preferred Education and Experience Previous experience handling technical support for a SaaS company Ability to speak Spanish fluently (not required, but would be considered a plus for the officials we support who do not speak English) To Apply We will only accept applications through our online job application form. Applications will be reviewed on a rolling basis until the position is filled.  For questions, please contact us at jobs@assignr.com. No phone calls, please. To apply: https://weworkremotely.com/remote-jobs/assignr-llc-customer-support-specialist-2

Headquarters: New York, NY URL: https://lemon.io Are you a talented Senior Developer looking for a remote job that lets you show your skills and get decent compensation? Look no further than Lemon.io — the marketplace that connects you with hand-picked startups in the US and Europe. What we offer: The rate depends on your seniority level, skills and experience. We've already paid out over $11M to our engineers. No more hunting for clients or negotiating rates — let us handle the business side of things so you can focus on what you do best. We'll manually find the best project for you according to your skills and preferences. Choose a schedule that works best for you. It’s possible to communicate async or minimally overlap within team working hours. We respect your seniority so you can expect no micromanagement or screen trackers. Communicate directly with the clients. Most of them have technical backgrounds. Sounds good, yeah? We will support you from the time you submit the application throughout all cooperation stages. Most of our projects involve working in a fast-paced startup environment. We hope you like it as much as we do. Through our community, we will connect you with the best developers from more than 50 countries. Currently, we have multiple requests, so please check the requirements. Requirements: 4+ years of commercial experience in software development 3+ years of commercial experience with .NET 3+ years of commercial experience with React Expertise in TypeScript is required 2+ years of experience with Microsoft Azure is a plus You should also have: Strong technical skills: as a Senior Developer, you are expected to be able to create projects from scratch and have a deep understanding of application architecture. Clear and effective communication in English — advanced ability to discuss business tasks, justify decisions, and communicate issues. Good self-presentation is also essential for upcoming client calls. Strong self-organizational skills — ability to work full-time remotely with no supervision. Reliability — we want to trust you and expect that you won’t let us and the client down. Adaptability and Flexibility — the ability to onboard the project promptly after accepting it and start delivering results quickly. Sounds good for you? Apply now and join the Lemon.io community! NOT YOUR TECH STACK? We're placing Senior Developers (4+ yrs commercial experience) across AI Agent Architecture, AI Automation Architecture, AI Engineering, Site Reliability Engineering, Platform Engineering, React & Python, React & Golang, Golang, React & Java, React & Ruby, Ruby, PHP & Vue, Vue & Node.js, Android & iOS, iOS & Swift, Flutter & Firebase, Solutions Engineer, Blockchain (Ethereum/Ethers.js/Wagmi/Viem/Solana), Angular & Node.js, Vue & .NET, Python & Vue, DevOps, MLOps, Data Science, Angular & PHP, Angular & .NET, Symfony & React, Symfony & Vue, Symfony & Angular, Python, Symfony & JavaScript & Next.js & TypeScript, Data Analysis, React & PHP, Data Engineering, Project Management, Product Management, QA Automation, QA Manual, Embedded Software Engineering, React Native & Node, React Native & React, React Native & Ruby, React Native & Python, WordPress, Android, Data Annotation, Angular, Python & Node, React & Node.js, Java & Spring, Svelte & Python, Svelte & Node.js, Svelte & TypeScript, Rust, Shopify & JavaScript, Vue & Nuxt, PHP & Laravel, UI/UX Design, Animation, Graphic Design, React & Node & React Native, .NET & C#, Electron, Scala, C++, Unreal Engine & C++, Python & LLM, Unity, or Machine Learning Engineering and more. Reach out and we'll match you. P.S. We work with developers from 50+ countries in different regions: Europe, LATAM, Asia (Philippines, Indonesia), Oceania (Australia, New Zealand, Papua New Guinea), Canada, and the UK. However, we have some exceptions. At the moment, we don’t have a legal basis to accept applicants from the following countries: European: Iceland, Liechtenstein, Kosovo, Belarus, Russia, and Serbia. Latin America: Cuba and Nicaragua Most Asian countries and Africa. We expand and shorten the list of exemptions regularly. To apply: https://weworkremotely.com/remote-jobs/lemon-io-senior-net-full-stack-developer-1

Headquarters: Mexico URL: http://lawnstarter.com About LawnStarter LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M 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 Analytics at LawnStarter We're a small, senior analytics team supporting the entire company - product, marketing, operations, and finance all run on the data we serve. The foundation is solid: a centralized Redshift data warehouse where all source data lands, modeled in dbt and orchestrated by Airflow, with Segment feeding event data in. You won't be stitching scattered sources together - the platform exists; your job is to make it trustworthy and keep it that way. We're mid-migration to Lightdash as our single BI platform, replacing Tableau and Metabase. Here's the honest gap: everyone on the team today is an analyst. Data quality, tracking standards, and platform hygiene get done as side work, squeezed between analyses. Nobody wakes up thinking about them - which is exactly the job we're hiring for. The Role 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. 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. Problems to Solve Make the Lightdash migration a step-change, not a re-platforming We're replacing Tableau and Metabase with Lightdash. Done poorly, we trade two messy tools for one messy tool. You'll design the structure - spaces, permissions, certification, naming - that lets stakeholders self-serve at the speed the company needs without creating an uncontrolled dashboard-growth nightmare. The hard part: autonomy and tidiness pull in opposite directions, and you have to deliver both. Finish and defend the semantic layer We just shipped our semantic layer for our most critical metrics - one governed definition per metric, so "two dashboards, two numbers" can't happen. The unglamorous truth: a long tail of metrics still needs definition and mapping, and a semantic layer only stays trustworthy if someone curbs its growth. You'll own both - extending coverage and keeping one-metric-one-definition true as the layer scales. Tame event-tracking entropy Segment events power our funnels and product analytics, and they're implemented by many engineers across many teams. The guardrails exist - a governed event catalog with naming standards, a property dictionary, a review lifecycle, and automated drift detection against production. What's missing is a dedicated owner: someone who holds every new event to the standard, keeps the catalog matched to what production actually sends, and evolves the guardrails as tracking grows. Without that, entropy wins - events drift and silently degrade when features change. Get ahead of breakage instead of chasing it Today, when production data changes upstream, we too often find out when a pipeline breaks or a stakeholder flags a wrong number. You won't start from zero - an AI-powered Analytics Engineer agent already runs freshness monitoring, metric anomaly detection, and dbt-based lineage checks - but it doesn't yet run at the scale or coverage we need. You'll take detection from partial to comprehensive, extend lineage beyond dbt (Segment events and Lightdash need stitching in), and wire it into engineering's change review, so a proposed production change comes with a downstream impact assessment instead of a postmortem. The end-state is data contracts: breaking changes caught in engineering's workflow, not ours. What Success Looks Like (Year 1) Zero pipeline incidents from unannounced source-data changes - lineage and automation catch them before they break anything downstream, and production changes ship with an impact assessment instead of a postmortem. Zero freshness incidents - stakeholders never open a stale dashboard. Every area of the business manages on official, well-maintained metrics and dashboards - product, marketing, ops, and finance self-serve in Lightdash against a fully mapped semantic layer; Tableau and Metabase are retired; arguments about whose number is right don't happen. Not because you built the dashboards - because you built the system that keeps them trustworthy. Every Segment event has an owner and a standard - new events ship compliant, and degradation gets caught automatically, not by accident. Governance runs as a system - documented processes that would survive you taking a month off. Requirements Who You Are Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy - you're the person who can't leave a broken naming convention alone. This is unlikely to be a good fit if you see governance as a stepping stone to "real" analytics work. AI-native. You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go - one person covering ground that used to take a team. You also see the reverse direction: AI agents consume our data daily, and making the warehouse safe and legible for them is part of governance now. This is unlikely to be a good fit if you're skeptical of AI tools or prefer to do everything manually. A hands-on senior operator. You write the SQL, debug the Airflow DAG, and configure the permissions yourself - seniority here means judgment and speed, not delegation. This is unlikely to be a good fit if your last few years were spent directing others and you'd need a team to execute. Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. This is unlikely to be a good fit if your quality practice depends on manual review and discipline. An enforcer people actually like. You'll hold engineers and analysts you don't manage to standards - which takes clear rules, good tooling that makes compliance easy, and the spine to say no gracefully. This is unlikely to be a good fit if you avoid friction or, at the other extreme, enjoy being the department of no. This Role Is NOT A people-management role - yet. You'll work alone for a while. A team may grow under you if the scope demands it, but if you need direct reports on day one, this isn't it. A policy or committee job. There are no governance councils to chair and no binders to produce. When something's broken, you fix it - with code, config, or a conversation. A BI analyst role. You won't spend your days building dashboards for stakeholders. You build the platform and guardrails that let everyone else do that well. A finished system to babysit. Much of this doesn't exist yet. If you want to operate a mature data platform rather than build one, you'll be frustrated here. Tech You'll Touch Warehouse & pipelines - Redshift, dbt, Airflow Event tracking - Segment BI - Lightdash (primary), Tableau and Metabase (sunsetting) AI tooling - Claude Code, Codex, Brain (our internal AI toolkit), and any tool that makes you more effective or efficient Observability - an AI-powered Analytics Engineer agent (freshness monitoring, anomaly detection, dbt lineage) you'll scale up, plus the quality and impact tooling you'll add around it You don't need every box checked. You need hands-on depth in the warehouse/pipeline layer and credible experience keeping a BI tool and tracking plan healthy at company scale. Benefits Compensation & Benefits Base salary: $75k-$120k/year Fully remote: This work needs deep focus, building monitors, untangling pipelines, and we trust you to manage your environment. Async collaboration is the norm. 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-data-governance-and-platform-manager-1

Headquarters: Brazil URL: http://lawnstarter.com About LawnStarter LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M 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 Analytics at LawnStarter We're a small, senior analytics team supporting the entire company - product, marketing, operations, and finance all run on the data we serve. The foundation is solid: a centralized Redshift data warehouse where all source data lands, modeled in dbt and orchestrated by Airflow, with Segment feeding event data in. You won't be stitching scattered sources together - the platform exists; your job is to make it trustworthy and keep it that way. We're mid-migration to Lightdash as our single BI platform, replacing Tableau and Metabase. Here's the honest gap: everyone on the team today is an analyst. Data quality, tracking standards, and platform hygiene get done as side work, squeezed between analyses. Nobody wakes up thinking about them - which is exactly the job we're hiring for. The Role 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. 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. Problems to Solve Make the Lightdash migration a step-change, not a re-platforming We're replacing Tableau and Metabase with Lightdash. Done poorly, we trade two messy tools for one messy tool. You'll design the structure - spaces, permissions, certification, naming - that lets stakeholders self-serve at the speed the company needs without creating an uncontrolled dashboard-growth nightmare. The hard part: autonomy and tidiness pull in opposite directions, and you have to deliver both. Finish and defend the semantic layer We just shipped our semantic layer for our most critical metrics - one governed definition per metric, so "two dashboards, two numbers" can't happen. The unglamorous truth: a long tail of metrics still needs definition and mapping, and a semantic layer only stays trustworthy if someone curbs its growth. You'll own both - extending coverage and keeping one-metric-one-definition true as the layer scales. Tame event-tracking entropy Segment events power our funnels and product analytics, and they're implemented by many engineers across many teams. The guardrails exist - a governed event catalog with naming standards, a property dictionary, a review lifecycle, and automated drift detection against production. What's missing is a dedicated owner: someone who holds every new event to the standard, keeps the catalog matched to what production actually sends, and evolves the guardrails as tracking grows. Without that, entropy wins - events drift and silently degrade when features change. Get ahead of breakage instead of chasing it Today, when production data changes upstream, we too often find out when a pipeline breaks or a stakeholder flags a wrong number. You won't start from zero - an AI-powered Analytics Engineer agent already runs freshness monitoring, metric anomaly detection, and dbt-based lineage checks - but it doesn't yet run at the scale or coverage we need. You'll take detection from partial to comprehensive, extend lineage beyond dbt (Segment events and Lightdash need stitching in), and wire it into engineering's change review, so a proposed production change comes with a downstream impact assessment instead of a postmortem. The end-state is data contracts: breaking changes caught in engineering's workflow, not ours. What Success Looks Like (Year 1) Zero pipeline incidents from unannounced source-data changes - lineage and automation catch them before they break anything downstream, and production changes ship with an impact assessment instead of a postmortem. Zero freshness incidents - stakeholders never open a stale dashboard. Every area of the business manages on official, well-maintained metrics and dashboards - product, marketing, ops, and finance self-serve in Lightdash against a fully mapped semantic layer; Tableau and Metabase are retired; arguments about whose number is right don't happen. Not because you built the dashboards - because you built the system that keeps them trustworthy. Every Segment event has an owner and a standard - new events ship compliant, and degradation gets caught automatically, not by accident. Governance runs as a system - documented processes that would survive you taking a month off. Requirements Who You Are Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy - you're the person who can't leave a broken naming convention alone. This is unlikely to be a good fit if you see governance as a stepping stone to "real" analytics work. AI-native. You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go - one person covering ground that used to take a team. You also see the reverse direction: AI agents consume our data daily, and making the warehouse safe and legible for them is part of governance now. This is unlikely to be a good fit if you're skeptical of AI tools or prefer to do everything manually. A hands-on senior operator. You write the SQL, debug the Airflow DAG, and configure the permissions yourself - seniority here means judgment and speed, not delegation. This is unlikely to be a good fit if your last few years were spent directing others and you'd need a team to execute. Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. This is unlikely to be a good fit if your quality practice depends on manual review and discipline. An enforcer people actually like. You'll hold engineers and analysts you don't manage to standards - which takes clear rules, good tooling that makes compliance easy, and the spine to say no gracefully. This is unlikely to be a good fit if you avoid friction or, at the other extreme, enjoy being the department of no. This Role Is NOT A people-management role - yet. You'll work alone for a while. A team may grow under you if the scope demands it, but if you need direct reports on day one, this isn't it. A policy or committee job. There are no governance councils to chair and no binders to produce. When something's broken, you fix it - with code, config, or a conversation. A BI analyst role. You won't spend your days building dashboards for stakeholders. You build the platform and guardrails that let everyone else do that well. A finished system to babysit. Much of this doesn't exist yet. If you want to operate a mature data platform rather than build one, you'll be frustrated here. Tech You'll Touch Warehouse & pipelines - Redshift, dbt, Airflow Event tracking - Segment BI - Lightdash (primary), Tableau and Metabase (sunsetting) AI tooling - Claude Code, Codex, Brain (our internal AI toolkit), and any tool that makes you more effective or efficient Observability - an AI-powered Analytics Engineer agent (freshness monitoring, anomaly detection, dbt lineage) you'll scale up, plus the quality and impact tooling you'll add around it You don't need every box checked. You need hands-on depth in the warehouse/pipeline layer and credible experience keeping a BI tool and tracking plan healthy at company scale. Benefits Compensation & Benefits Base salary: $75k-$120k/year Fully remote: This work needs deep focus, building monitors, untangling pipelines, and we trust you to manage your environment. Async collaboration is the norm. 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-data-governance-and-platform-manager