Open Roles

We are seeking a data-driven, process-focused Senior Manager of Revenue Operations to lead and scale our GTM infrastructure and analytics across Sales, Marketing, and Customer Success. This role is ideal for someone who thrives in high-growth SaaS environments, has deep CRM and data expertise, and can drive operational excellence through collaboration and execution.  You will serve as the operational owner of GTM systems, processes, and reporting, helping to align revenue teams toward shared goals, eliminate friction, and ensure consistent pipeline and retention growth. This role reports into the Chief Marketing Officer and has strong cross-functional alignment with the VP of Sales, Chief Customer Officer and CFO. This role has one direct report – the business applications administrator.  Key Responsibilities  GTM Systems & Processes  Own and optimize core GTM systems (e.g., Salesforce, HubSpot, TaskRay, Salesloft, etc.).  Design and enforce scalable processes across lead management, opportunity tracking, renewals, and customer lifecycle management.  Manage system integrations and data flows between CRM, marketing automation, support, and billing systems.  Sales Operations   Partner closely with Sales leadership to design, implement, and evolve sales processes aligned with growth targets.  Maintain and optimize territory planning, lead routing, and sales team capacity models.  Support compensation planning and administration in partnership with Finance and HR.  Oversee quote-to-cash workflows, contract management process, approval processes, and deal desk operations.  Deliver operational insights to drive rep productivity and sales efficiency.  Reporting & Analytics  Build and maintain dashboards for GTM KPIs, pipeline health, revenue forecasting, churn analysis, and team performance.  Prepare weekly, monthly and quarterly reporting for leadership.  Support leadership with board-ready data visualizations and insights.  Cross-Functional Enablement  Collaborate with Sales, Marketing, CS, and Product Marketing to drive GTM alignment and campaign execution.  Develop playbooks, process documentation, and training for GTM tools and workflows.  Lead post-mortems and continuous improvement loops across deals and campaigns.  ‍ Team Leadership  Manage and mentor Business Applications Administrator to drive improvement across business technology.  Set priorities, coach on systems and data modeling, and promote a proactive, service-oriented culture.  Serve as point of contact for tool vendors and budget planning for the RevOps stack.  Qualifications  Required Experience 5–7 years of experience in RevOps, Sales Ops, or Marketing Ops, ideally in a B2B SaaS company.  2+ years managing people or leading cross-functional initiatives.  Expertise in CRM administration (Salesforce preferred), GTM systems, and data analytics tools.  Strong understanding of SaaS funnel metrics (CAC, LTV, pipeline coverage, NRR, churn).  Highly organized, execution-oriented, and comfortable managing competing priorities.  Strong orientation towards using technology, automation and AI to optimize processes across the GTM organization.  Preferred Experience Experience supporting multiple GTM functions (Sales, CS, Marketing).  Experience with payments or utilization based business models  Familiarity with quote-to-cash workflows and CPQ tools. 

We are seeking a detail-oriented Product Owner (Contractor) to support the execution of AI and automation initiatives across our Central platform. This role is focused on delivery, documentation, and backlog ownership, not product strategy or roadmap direction. The Product Owner will work closely with Product Managers and Engineering teams to translate defined product priorities into clear user stories, workflow documentation, and detailed requirements that enable successful delivery of AI-powered automation capabilities.  This is a remote opportunity. We are seeking contractors located in LATAM who are comfortable working in an English-speaking professional environment. Key Responsibilities Backlog Ownership & Agile Execution  Own and manage the delivery backlog for AI and automation-related product work Write detailed user stories, functional requirements, and acceptance criteria Break down initiatives into well-scoped epics, stories, and tasks Support Agile ceremonies including sprint planning, refinement, reviews, and retrospectives Partner with Engineering to clarify scope and remove blockers  AI & Automation Workflow Enablement  Analyze and document end-to-end workflows suitable for AI-driven automation Translate operational processes into clear, executable automation requirements Define business rules, inputs, outputs, dependencies, and exception handling Support AI-enabled capabilities including ingestion, reconciliation, orchestration, and GenAI assistants Ensure workflows are well-documented and testable  Qualifications  2+ years of experience in a Product Owner, Business Analyst, or delivery-focused role Strong experience writing user stories and supporting Agile teams Prior experience with AI, automation, or SaaS platforms is a must Experience in FinTech or Payments is a plus Excellent written and verbal communication skills 

The Tech Lead for the AI Pod is responsible for translating product ideas and data science capabilities into production-ready AI solutions. This role partners closely with Product Management and the Data Science Leader to rapidly design, prototype, and ship AI-driven features that deliver measurable business value. This is a highly hands-on technical leadership role focused on speed, pragmatism, and production quality, balancing experimentation with scalable engineering practices.   This is a remote opportunity. We are seeking contractors located in LATAM who are comfortable working in an English-speaking professional environment. Key Responsibilities  AI Solution Delivery & Architecture  Lead the technical design and implementation of AI-powered product features from concept through production.  Own end-to-end architecture for AI solutions, including data flows, model integration, APIs, and application integration.  Make pragmatic decisions to accelerate delivery while maintaining system integrity.  Ensure AI solutions are secure, observable, scalable, and aligned with platform standards.  Pod Leadership & Execution  Act as the technical lead for a cross-functional AI Pod.  Break down product requirements into executable technical workstreams and prototypes.  Guide rapid iteration cycles, proofs-of-concept, and MVPs, balancing experimentation with production readiness.  Review code, architecture, and technical decisions to maintain quality and velocity.  Product & Data Collaboration  Partner closely with Product Management to shape problem definitions, success metrics, and delivery plans.  Collaborate with the Data Science Leader to integrate models, analytics, and data assets into product workflows.  Translate data science outputs into consumable APIs, services, and product features.  Provide technical feedback on feasibility, scope, and tradeoffs during product discovery.  Operationalization & Quality  Ensure AI features are production-grade, including monitoring, logging, and performance tracking.  Implement guardrails around AI usage, including reliability, latency, cost controls, and failure modes.  Support experimentation frameworks, A/B testing, and post-launch learning loops.  Drive responsible AI practices, including explainability, bias awareness, and data privacy considerations.  Technical Standards & Enablement  Define and enforce lightweight engineering standards for AI-enabled systems.  Promote reuse of components, prompts, pipelines, and services across AI initiatives.  Mentor pod engineers on AI-adjacent system design and best practices.  Contribute to internal documentation and shared AI patterns/playbooks.    Required Qualifications  BS or MS in Computer Science, Engineering, or related technical field.  8+ years of software engineering experience, including leading complex systems.  Strong experience designing and building production APIs and backend services.  Hands-on experience integrating ML models or LLMs into production systems.  Proficiency in Python and at least one backend language (e.g., Java, Node.js, Go).  Experience with cloud-native architectures (AWS, GCP, or Azure).  Solid understanding of data pipelines, model serving, and system observability.  Ability to work closely with product teams in fast-moving, iterative environments.    Preferred Qualifications  Experience working in AI-first or data-driven product teams.  Familiarity with modern LLM platforms, prompt engineering, and agent frameworks.  Experience operationalizing ML models (model serving, monitoring, versioning).  Exposure to experimentation platforms, feature flags, and A/B testing.  Experience in Agile or product-led development environments.    What Success Looks Like  AI features move from idea to production quickly and reliably.  Product teams trust the AI Pod to deliver pragmatic, high-impact solutions.  Data science outputs are effectively operationalized into real user workflows.  AI systems are observable, maintainable, and cost-effective.  The organization builds confidence and momentum around AI innovation.