Jobs at Northbeam

Own Northbeam’s end-to-end product design across AI features, incrementality, actionable data insights, integrations, and complex data platforms. Develop UX/UI vision, conduct customer research, create flows, wireframes, prototypes, and high-fidelity designs, and partner with product, data science, engineering, and marketing teams to ship experiences. Mentor designers and communicate design decisions to customers and executives.

Own the technical health of existing customer accounts, including configuration, tracking, integrations, data ingestion, and reporting pipelines. Diagnose data quality issues and technical risks, resolve problems or coordinate escalations, and communicate findings to customer-facing and technical teams. Develop health checks, documentation, and runbooks while identifying recurring issues for standardization and product improvement.

Own an enterprise customer book of business, supporting onboarding, integrations, troubleshooting, support tickets, data reviews, and product feedback. Monitor account health and usage data to identify churn risks, drive retention and expansion, and improve customer engagement. Partner cross-functionally with Product, Engineering, Onboarding, Support, and Media Strategy to resolve technical issues, coordinate deliverables, and enhance customer success processes.

Own an enterprise customer portfolio, supporting onboarding, product integrations, troubleshooting, account health monitoring, retention, and expansion. Coordinate with Product, Engineering, Support, Onboarding, and Media Strategy teams to resolve issues, deliver recurring insights, manage feedback, and improve customer engagement. Use analytics and CRM tools to identify risks, automate health tracking, and help customers maximize Northbeam’s marketing intelligence platform.

Advise eCommerce clients on media buying, budget allocation, creative strategy, attribution, and growth across Meta, Google, TikTok, and other channels. Conduct data reviews and growth audits, translate attribution and marketing mix modeling data into actionable recommendations, support onboarding and retention, and guide experimentation, scaling, and LTV:CAC improvement. Collaborate cross-functionally to communicate customer needs and develop repeatable benchmarking, goal-setting, and executive reporting playbooks.

Advise eCommerce clients on media buying, budget allocation, creative strategy, attribution, and growth frameworks across paid channels. Conduct data reviews and growth audits, translate attribution and MMM insights into recommendations, support onboarding and retention, and develop repeatable reporting and benchmarking playbooks. Collaborate with Product, Marketing, and Customer Success teams to communicate customer needs and improve the platform.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from ad platforms, order systems, and real-time events. Ensure reliability, observability, and maintainability across event-driven and batch architectures, implement authentication flows, optimize data workflows in a cloud-native environment, and collaborate with product, infrastructure, and data teams to onboard new sources.

Design and build distributed backend systems, microservices, and near-real-time data ingestion and processing. Scale an incrementality experimentation platform, add AI-powered workflows, extend data integrations and partner exports, and contribute frontend components as needed to deliver end-to-end product features.

Design and implement scalable backend and distributed systems for a marketing intelligence platform. Build near-real-time ingestion and processing, scale an incrementality/experimentation product, integrate AI-powered workflows, improve data ingestion/exports and connection-state systems, and contribute frontend components to deliver end-to-end product experiences.

Lead large, complex B2B SaaS product initiatives from concept to launch, owning roadmap, specs, UAT, and go-to-market enablement. Work closely with engineering, data science, and design to embed AI and measurement capabilities (MTA, MMM, incrementality) into product experiences, and engage directly with customers to validate and iterate.

Senior product leader owning company roadmap and driving flagship, data- and AI-driven product initiatives end-to-end. Work directly with customers, partner closely with design, engineering, and data science, manage and elevate senior PMs, run development sprints and UAT, and equip GTM teams. Hands-on player-coach role focused on shipping AI and measurement features for marketing attribution and analytics.

Build and own end-to-end fullstack features for a marketing analytics dashboard. Lead technical direction, collaborate with product and customer teams, optimize frontend performance, create and maintain APIs, ensure reliability and scalability, and mentor other engineers.

Design, develop, and deliver fullstack web solutions, optimize platforms, mentor engineers, and manage product features from ideation to launch.

Lead development and operationalization of measurement products (MMM, incrementality, insights, recommendations). Build, deploy, and maintain production-grade statistical and ML systems, debug end-to-end data and model issues, evaluate methodologies, and partner with engineering, product, and customer teams to improve measurement accuracy and reliability. Provide customer-facing explanations and support for measurement outputs.

Design, build, deploy, and maintain production-grade measurement and ML systems (MMM, incrementality, insights, recommendations). Translate statistical and causal methods into scalable products, debug full-stack data and model issues, evaluate methodology tradeoffs, and collaborate with engineering, product, and customer teams while providing customer-facing technical support.