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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, workflows, and scripts that automate customer data ingestion and validation. Develop distributed data platform systems integrating ad networks, warehouses, and partner tools; contribute to AI services; collaborate with Customer Success on customer data models; and participate in on-call support.

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.

Design and lead backend services and data pipelines for Haus’s data platform, including high-throughput ingestion, normalization, orchestration, distributed systems, warehouse modeling, dbt architecture, data quality, lineage, and BigQuery optimization. Set technical standards for testing, APIs, observability, and CI/CD; guide architecture across the GCP stack; mentor engineers; and influence company-wide data strategy while partnering with engineering, product, and data science teams.

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.

Lead the design, development, optimization, and productionization of machine learning systems, particularly causal marketing mix modeling (cMMM). Build reusable statistical libraries and scalable ML pipelines, implement probabilistic techniques and agentic workflows, review designs and code, collaborate cross-functionally, and mentor ML engineers. The role requires expertise in production ML systems, statistical modeling, experimentation, and object-oriented programming.

Lead end-to-end development and productionization of machine learning systems for causal marketing measurement and optimization, especially cMMM. Build probabilistic and statistical libraries, maintain ML pipelines, implement agentic AI workflows and model validation, review designs and code, collaborate cross-functionally, and mentor ML engineers.

Own marketing measurement models for multiple customers, partnering with strategy managers from kickoff through model release, interpretation, enablement, and action planning. Configure and QA model inputs and outputs, manage escalations, deliver deep-dive analyses and scenario work, and support renewals and account growth. Build scalable playbooks, documentation, and AI-assisted workflows while translating complex measurement results into actionable budgeting and campaign decisions. Provide customer feedback to product, science, and engineering teams and support pre-sales and case studies.