Jobs at Northbeam

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Own end-to-end technical onboarding for new customers, including product configuration, permissions, workflows, API integrations, data migration, troubleshooting, and go-live. Manage implementation timelines, milestones, risks, and deliverables while coordinating with customers and internal Sales, Customer Success, Product, Support, and Engineering teams. Contribute to onboarding documentation and playbooks.

Build backend services and APIs primarily in Python, with frontend contributions in React and TypeScript. Develop experimentation platforms, near-real-time data ingestion and processing, AI-powered workflows, partner integrations, RBAC, and other product capabilities. Collaborate with product managers, designers, data scientists, and senior engineers while delivering production-ready features across backend, frontend, pipelines, and infrastructure.

Build and maintain reliable data pipelines, APIs, integrations, and event-driven or batch processing systems. Transform data from advertising, ecommerce, order management, and event sources while improving monitoring, freshness, performance, and scalability. Collaborate with data engineering, infrastructure, product, and business teams to extend the integration platform in a cloud-native, multi-tenant environment.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Lead event-driven and batch processing decisions, establish standards for observability, freshness, and failure handling, and guide cross-functional platform design. Mentor engineers, conduct design reviews, influence teams without direct authority, and drive foundational platform improvements. The role requires deep Python and SQL expertise, cloud data warehouse experience, scalable API-based ETL design, orchestration, and containerized infrastructure experience.

Lead backend-focused software architecture and development for data-intensive products, including experimentation platforms, near-real-time ingestion, AI workflows, integrations, APIs, and distributed systems. Partner with product, design, and data science teams to translate ambiguous needs into scalable production solutions. Contribute across frontend, pipelines, and infrastructure, while mentoring engineers, conducting design reviews, and setting technical direction.

Lead backend-focused software architecture and development across distributed systems, data ingestion, experimentation platforms, AI workflows, and product capabilities. Build primarily in Python while contributing to React and TypeScript frontend work. Translate ambiguous product needs into scalable production systems, guide technical direction, partner with product and data teams, mentor engineers, and improve engineering practices.

Build backend services, APIs, data-processing systems, and frontend features for Northbeam’s marketing intelligence platform. Work on experimentation, near-real-time data infrastructure, AI-powered workflows, data integrations, RBAC, and end-to-end product delivery. Collaborate with data scientists, product managers, designers, and senior engineers while contributing across backend, frontend, pipelines, and infrastructure in a fast-moving environment.

Build and maintain reliable data pipelines, integrations, APIs, and event-driven or batch processing systems. Transform data from advertising, commerce, and order management platforms while improving monitoring, data freshness, performance, security, and scalability. Partner with product, infrastructure, and data engineering teams to expand the integration platform and onboard new sources in a cloud-native, multi-tenant environment.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Design scalable API-based ETL, event-driven and batch processing systems; establish standards for observability, freshness, and failure handling; and guide build-versus-buy decisions. Collaborate across engineering, infrastructure, product, and business teams while mentoring engineers, leading design reviews, and driving complex platform initiatives to completion.

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.