Jobs at Haus.io

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.

Lead architecture and technical direction for Haus's data ingestion and normalization platform. Design systems for schema evolution, data contracts, data quality, lineage, reproducible pipelines, and observability. Drive GCP/BigQuery/dbt decisions, mentor engineers, own incident response, and partner with data science and product to ensure reliable data foundations and AI-driven data-quality workflows.

Own end-to-end customer onboardings for cMMM and GeoLift, run data onboarding calls, troubleshoot data and warehouse connections using SQL and Retool, file and track bug reports, collaborate with Product/Data/Science/Engineering, document processes, and apply AI tooling to automate recurring tasks.

Build and maintain the backend science platform powering causal experiment analysis: data pipelines from BigQuery, statistical estimation library, Metaflow orchestration on Kubernetes, monitoring and reliability, collaborate with applied scientists, publish results to product APIs, use AI-assisted developer tools, and participate in on-call rotation.

Build and evolve a production Flask + React platform powering ad-experimentation and causal-inference products. Own end-to-end features from API and migrations to frontend visualizations, integrate and harden ad-platform APIs, collaborate with scientists and data engineers, use AI dev tools to accelerate work, participate in on-call rotation, and improve architecture incrementally while shipping continuously.