Jobs at Haus.io

Build Haus’s Architect product end to end, developing Python backend services that convert model outputs into recommendations and React interfaces for customer approval. Integrate with platform systems, define API contracts, manage production reliability, and collaborate with product, science, and design. The role requires strong backend engineering, frontend proficiency, cloud operations, database experience, product judgment, testing, monitoring, and effective use of AI coding tools.

Build and maintain backend services, APIs, data pipelines, long-running workflows, ingestion systems, validation layers, customer-facing tools, and AI services for Haus’s data platform. Partner with Customer Success on custom data models, automate customer data onboarding, support distributed systems, and participate in on-call rotation.

Own the strategy and execution of Haus’s enterprise demand generation engine. Responsibilities include defining ICP and segmentation, developing ABM programs, managing paid, content, email, events, webinars, and sponsorships, and partnering with Sales and Product Marketing. The role owns pipeline targets, budget, attribution, funnel reporting, and performance measurement while managing one direct report and agency relationships. This hands-on position builds demand generation infrastructure and repeatable enterprise pipeline from the ground up.

Design and lead backend services and data pipelines for Haus’s data platform, including high-throughput ingestion, distributed systems, normalization, orchestration, data contracts, warehouse and lakehouse modeling, dbt architecture, data quality, lineage, observability, and BigQuery optimization. Set technical direction, establish engineering standards, mentor senior engineers, and collaborate with engineering, product, and data science leadership.

Leads end-to-end development and productionization of machine learning systems for causal marketing measurement and optimization, especially cMMM. Implements probabilistic and statistical methods, builds reusable ML libraries and pipelines, collaborates across engineering, product, and science teams, reviews designs and code, develops agentic ML workflows, and mentors machine learning engineers.

Own marketing measurement models for multiple customers, including configuration, QA, updates, interpretation, escalation support, and action planning. Partner with strategy managers to translate causal MMM results into budgeting and campaign decisions. Develop scalable playbooks, documentation, AI-assisted workflows, and customer-facing analyses that support value realization, renewals, pre-sales, and product feedback. Requires strong MMM, incrementality testing, customer communication, and analytical problem-solving experience in an ambiguous startup environment.

Manage a portfolio of customers to design, run, and interpret marketing experiments; translate learnings into budget and strategy decisions; advise on best practices; build stakeholder relationships; partner with Product/Science/Engineering to scale solutions; produce playbooks and support renewals and expansion.