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Full-stack Engineer

• Lead a team of 5-8 Fullstack developers.
• Write and review code (approximately 70/30 split in favor of management).
• Ensure the team follows company processes and engineering guidelines.
• Own the delivery of features and adherence to deadlines.
• Make technical decisions, prepare technical designs for new features, and align them with neighboring teams and your manager.
• Manage technical debt and maintain the technical backlog for your team.
• Mentor and develop engineers; conduct regular 1:1 meetings.
• Onboard new team members.
• Conduct performance reviews.

Engineering Team Leader, DX Platform

Revenue Operations

• Analyze customer and prospect behavior, product usage, and retention data to drive company strategy
• Create and maintain dashboards and reports that track key success metrics
• Collaborate with Finance to build models and forecasts that inform strategic decisions
• Partner with GTM and Product teams to create and implement initiatives that enhance onboarding, engagement, and customer satisfaction
• Plan, manage, and deploy customer and/or prospect territories
• Effectively communicate data-driven findings and strategic recommendations to key stakeholders
• Design and implement process improvements to drive scalability

Senior Revenue Operations Analyst

Data Scientist

• Lead the core product analytics team: define its mission, priorities, and roadmap
• Build and scale the team: participate in hiring, mentor analysts, plan workloads, and personally handle complex analytical and research tasks
• Design and develop shared analytical frameworks (e.g. metrics trees) and run cross-product, growth-focused research to generate insights applicable across multiple funnels and products
• Own company-level BI dashboards, including data architecture, data quality, and a unified metrics language
• Establish collaboration processes between core analytics, product teams, and other analysts across the company

Product Analytics Team Lead, Core

• Lead the core product analytics team: define its mission, priorities, and roadmap
• Build and scale the team: participate in hiring, mentor analysts, plan workloads, and personally handle complex analytical and research tasks
• Design and develop shared analytical frameworks (e.g. metrics trees)Run cross-product, growth-focused research and generate insights applicable across multiple funnels and products
• Own company-level BI dashboards, including data architecture, data quality, and a unified metrics language
• Establish collaboration processes between core analytics, product teams, and other analysts across the company

Product Analytics Team Lead – Core

Product Analyst

• Uncover Actionable Insights: Identify key trends in user behavior, develop data-backed product hypotheses, and work with teams to test them.
• Own A/B Testing: Design, launch, and analyze A/B tests to validate ideas and optimize product performance.
• Build a Data Foundation: Create and maintain data marts and BI reports to visualize product and operational metrics for the entire company.
• Perform Deep-Dive Analyses: Handle ad-hoc analytical requests to answer critical business and product questions.

Solutions Engineer

• Actively engage in presales and onboarding activities, ensuring successful implementations and customer satisfaction for large complex enterprise clients.
• Work closely with sales and customer success teams to develop strategies to drive revenue growth.
• Collaborate with product teams to ensure customer feedback is incorporated into product development.
• Work closely with other teams, such as Customer Success, Sales, Product and Support to ensure alignment and successful execution of projects.
• Develop and maintain relationships with key customer stakeholders, including C-level executives.
• Ability to manage high-stress situations and work under pressure to deliver results, juggling many active implementations simultaneously.
• Respond to RFPs.
• Lead A/B Tests for customers working with our Analytics team.

Solution Engineer

Artificial Intelligence

• Process Deconstruction: Lead in-depth workshops with stakeholders (e.g., Marketing, Legal, Finance) to conduct precise mapping of current workflows, identifying specific bottlenecks ripe for agentic automation.
• Blueprint Creation: Design the detailed future state architecture for departmental functions, explicitly defining the collaboration protocols and task handoffs between human roles and specialized AI agents.
• Defining Autonomy: Establish the technical and procedural governance models required for agent autonomy, specifically setting rules for decision rights, error escalation triggers, and critical human-in-the-loop oversight points.
• Dual-Path Roadmap: Develop a detailed, twelve-month transformation roadmap that simultaneously delivers near-term, high-ROI AI solutions while defining the technical prerequisites for achieving fully autonomous agentic operations.
• Agent Architecture Design: Define precise technical requirements for scalable agent architecture, including the necessary reasoning frameworks, risk management systems, and knowledge retrieval interfaces required for complex decision-making.
• Build/Buy/Prioritize: Construct data-driven business cases for solutions, explicitly contrasting build vs. buy decisions and prioritizing implementations based on technical feasibility and quantified ROI models (e.g., time savings, cost reduction).
• End-to-End Build Capability: Serve as a hands-on technical lead capable of building, testing, and deploying both interim (e.g., RAG-based systems) and highly complex agentic AI solutions, working alongside AI Solution Engineers.
• Operational Scaling: Oversee the technical integration and scaling of deployed AI agents within specific operational workflows (e.g., automating specific GTM tasks), ensuring seamless deployment with rigorous validation testing.
• Curriculum Development: Design, author, and deliver all technical and operational training curricula (e.g., documentation, specialized workshops) required to rapidly transition end-users into effective collaborators with the newly deployed AI agents.
• Impact Verification: Implement robust tracking mechanisms, utilizing specific KPIs (e.g., agent task completion rate, decision accuracy, departmental throughput change) to measure and verify the deployed solutions' business impact and ROI.

AI Transformation Lead

• Lead in-depth workshops with stakeholders (e.g., Marketing, Legal, Finance) to conduct precise mapping of current workflows, identifying specific bottlenecks ripe for agentic automation.
• Design the detailed future state architecture for departmental functions, explicitly defining the collaboration protocols and task handoffs between human roles and specialized AI agents.
• Establish the technical and procedural governance models required for agent autonomy, specifically setting rules for decision rights, error escalation triggers, and critical human-in-the-loop oversight points.
• Develop a detailed, twelve-month transformation roadmap that simultaneously delivers near-term, high-ROI AI solutions while defining the technical prerequisites for achieving fully autonomous agentic operations.
• Define precise technical requirements for scalable agent architecture, including the necessary reasoning frameworks, risk management systems, and knowledge retrieval interfaces required for complex decision-making.
• Construct data-driven business cases for solutions, explicitly contrasting build vs. buy decisions and prioritizing implementations based on technical feasibility and quantified ROI models (e.g., time savings, cost reduction).
• Serve as a hands-on technical lead capable of building, testing, and deploying both interim (e.g., RAG-based systems) and highly complex agentic AI solutions, working alongside AI Solution Engineers.
• Oversee the technical integration and scaling of deployed AI agents within specific operational workflows (e.g., automating specific GTM tasks), ensuring seamless deployment with rigorous validation testing.
• Design, author, and deliver all technical and operational training curricula (e.g., documentation, specialized workshops) required to rapidly transition end-users into effective collaborators with the newly deployed AI agents.
• Implement robust tracking mechanisms, utilizing specific KPIs (e.g., agent task completion rate, decision accuracy, departmental throughput change) to measure and verify the deployed solutions' business impact and ROI.