Role Description We are seeking a GTM Engineer to design, build, and optimize the systems that power our marketing and sales engine. You'll architect the technical infrastructure connecting our marketing tools, CRM, and analytics platforms, ensuring seamless data flow from first touch. You'll own HubSpot as the system of record and build the automated, signal-driven programs that fill it. You'll combine systems engineering expertise with business process design to build a scalable, data-driven revenue engine. Your impact - Pipeline Generation Systems - Build signal-based prospecting pipelines: identify buying triggers (funding, hiring, tech installs, product launches, job changes) and turn them into automated, scored outbound plays. - Own the enrichment and data layer - ICP scoring, account/contact data quality - so reps work on the right accounts, not just clean ones. - Build and maintain outbound infrastructure: domain/sender strategy, deliverability monitoring, sequencing, multichannel orchestration (email, LinkedIn, ads). - Use LLMs/AI agents in production workflows for research, qualification, personalization, and classification - with evaluation and guardrails against hallucinated output. - Instrument and iterate: measure reply, meeting, opp conversion by segment and play, and kill what doesn't work. - GTM Systems Architecture - Architect and maintain integrations between HubSpot, Google Ads, Google Analytics, Tag Manager, and marketing automation platforms. - Design data synchronization workflows that ensure consistent customer data across marketing, sales, and analytics systems. - Build automated processes that eliminate manual data entry and reduce time from lead capture to sales engagement. - Implement API connections and data pipelines that power real-time attribution and conversion tracking. - CRM Platform Engineering - Own technical administration of HubSpot: standard and custom objects, properties, workflows, pipeline automation, and data model design. - Implement workflow automation for lead routing, scoring, lifecycle progression, and sales handoff. - Design and enforce data quality mechanisms including deduplication rules, validation workflows, and data governance standards. - Marketing Attribution & Tracking Infrastructure - Design and implement end-to-end conversion tracking architecture from ad click through closed-won revenue. - Build UTM tracking standards, campaign tagging infrastructure, and multi-touch attribution models. - Implement tracking mechanisms across web analytics, ad platforms, and CRM to close attribution gaps. - Revenue Metrics & Business Logic - Collaborate with Revenue Operations Manager to translate business requirements into technical system configurations. - Partner with Analytics Engineer to build data models and dashboards for pipeline metrics, conversion rates, and revenue reporting. - Build data quality monitoring and alerting to catch tracking issues, attribution gaps, and process breakdowns. - Process Automation & Optimization - Identify manual processes and build technical solutions that automate repetitive GTM workflows. - Design scalable handoff mechanisms between marketing, sales, and customer success systems. - Build enablement documentation and technical guides for marketing and sales teams. Qualifications - 4+ years in Revenue Operations Engineering, GTM Systems, Marketing/Sales Operations, or technical business systems roles. - Proven experience architecting CRM platforms, marketing automation systems, and business system integrations. - Strong technical skills with APIs, webhooks, data flows, and system integration patterns. - Experience working cross-functionally with Marketing, Sales, RevOps, Analytics, and Engineering teams. - Expert-level hands-on experience with CRM platforms (HubSpot strongly preferred) including workflows, custom objects, APIs, and integrations. - Proficiency with marketing automation platforms and web analytics tools (Google Analytics, Tag Manager, ad platform APIs). - Understanding of data modeling, ETL patterns, and system architecture principles. - Comfortable with SQL for data validation and analysis. - Knowledge of data governance, quality management, and privacy compliance (GDPR, tracking consent). - Strong problem-solving abilities with systems thinking approach. - Excellent communication skills with both technical and business stakeholders. - Self-starter who thrives with high ownership and minimal oversight. - Deep understanding of B2B SaaS metrics: MRR, ARR, CAC, LTV, pipeline velocity, conversion rates. Benefits - Health insurance (medical, dental, vision) with 100% of employee premiums covered. - Unlimited PTO. - Flexible, remote WFH culture. - Access to the iconic Hearst Tower & its facilities in NYC (should you choose to ever be in person). - Based on previous experience and skillset, the starting base salary range for this position: $135,000 - $160,000.

• Independently deliver well-scoped customer-facing features and platform capabilities from design and implementation through rollout and follow-through in production. • Build reliable services, APIs, and web experiences, improving existing systems while shipping new capabilities. • Contribute to AI-powered workflows and experiences using modern LLM tooling, including evaluation, iteration, and human-in-the-loop patterns. • Make thoughtful technical tradeoffs across speed, maintainability, scalability, and customer impact. • Collaborate across Engineering, Product, Design, ML, and platform teams to deliver cohesive customer experiences. • Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices.

Role Description This is a full-time remote position that can be located anywhere in the U.S. Technical Leadership & Architecture - Serve as the technical authority for financial systems, billing platforms, and data solutions. - Define and maintain architectural standards, engineering principles, and technology strategies. - Design scalable, secure, highly available, and resilient distributed systems. - Lead architecture reviews and provide technical guidance across multiple engineering teams. - Identify and mitigate technical risks while balancing business objectives and long-term sustainability. Platform Engineering - Architect and evolve data ingestion pipelines supporting operational and analytical workloads. - Design and optimize high-volume transaction processing systems and pricing engines. - Guide development of cloud-native solutions leveraging AWS services and serverless architectures. - Ensure platform observability, performance, scalability, reliability, and operational excellence. - Drive modernization initiatives across applications, infrastructure, integrations, and data platforms. AI & Innovation - Champion adoption of Artificial Intelligence solutions, including: - Large Language Models (LLMs) - Retrieval-Augmented Generation (RAG) - Agentic AI Systems - AI-assisted software engineering practices - Identify and implement AI-driven capabilities that improve automation, operational efficiency, and business value. - Partner with Data Science teams to operationalize machine learning and advanced analytics solutions. Engineering Excellence - Establish and promote best practices for software architecture, design patterns, testing, security, and DevOps. - Conduct technical design reviews and mentor engineers at all levels. - Foster a culture of innovation, accountability, collaboration, and continuous improvement. - Drive engineering productivity through automation, low-code solutions, and platform tooling. - Lead incident response efforts for critical production issues and drive root-cause resolution. Strategic Partnership - Collaborate with executives, business stakeholders, and engineering leadership to align technology investments with strategic goals. - Translate complex technical concepts into business-focused recommendations. - Influence enterprise technology decisions and investment priorities. - Contribute to organizational technical strategy and long-term platform planning. Qualifications - Bachelor's degree in Computer Science, Information Systems, Engineering, or related field. - 10+ years of professional software engineering experience. - 5+ years of experience providing technical leadership across multiple teams or large-scale platforms. - Expert-level experience with Python. - Expert-level experience with C# and .NET Core. - Deep expertise in software architecture and distributed systems design. - Strong experience building cloud-native applications on AWS. - Advanced knowledge of PostgreSQL, Snowflake, and data platform architectures. - Extensive experience with SQL optimization, data modeling, and enterprise-scale data solutions. - Experience implementing event-driven architectures and microservices. - Experience leading large-scale modernization or transformation initiatives. - Hands-on experience with AI technologies, including LLMs, RAG, Agentic AI, or AI-powered enterprise solutions. Preferred - Experience with NetSuite and Salesforce integrations. - Experience with Low-Code platforms such as OutSystems. - Experience with financial systems, billing platforms, pricing engines, or revenue management solutions. - Experience building enterprise data lakes, analytics platforms, or data mesh architectures. - Experience establishing engineering standards and influencing technical direction across organizations. Benefits - Medical, Dental, Vision, Health Savings Accounts / Flexible Spending Accounts. - Life and AD&D Insurance. - 401(k). - Tuition Reimbursement. - An array of resources that encourage a lifetime of healthier living. - Compensation ranges from $118,600 - $165,000 annually, based on skills, experience, and education.

• Design and implement core platform services in Kotlin, building the abstractions described above across diverse infrastructure targets. • Solve difficult integration challenges at the boundary between platform software and virtualization infrastructure. • Collaborate with Staff and Principal Engineers on architectural decisions. • Own features and components end-to-end — from design through implementation, testing, and production operation. • Support other engineering teams that depend on Core Services APIs and abstractions. • Identify and drive resolution of performance bottlenecks, reliability issues, and technical debt within the Core Services stack.