Staff Engineer - Data Platform - Seattle

About Haus Haus is the causal marketing platform top businesses trust to optimize billions in ad spend worldwide. With support from PhD economists, data scientists, and growth experts, Haus’ AI-driven technology translates complex marketing measurement into clear action and outcomes, enabling brands like Dyson, Wayfair, Sonos, Fanduel, SharkNinja, and Intuit to optimize spend, accelerate growth, and make smarter marketing decisions at scale. The Role Haus's data engineering team powers the entire incrementality platform — every causal experiment, every marketing mix model, every dollar of ad spend we help our customers reallocate runs on the pipelines this team builds. We are looking for a Staff Software Engineer to set the technical direction for how Haus ingests data from ad networks, customer warehouses, and partner tools, and how we normalize it into a clean, trustworthy foundation for our data science research and customer-facing products. You will be the senior-most IC on a 6-10 person team, partnering directly with engineering leadership, data science, and product teams to make Haus's data platform a durable competitive advantage. What you’ll do - Be the tech-lead and architect for Haus's data ingestion and normalization platform — ad network APIs (Google, Meta, TikTok, Amazon, etc.), Fivetran connectors, and customer warehouses (Snowflake, BigQuery) — balancing throughput, cost, and reliability. - Design and lead implementation of high-leverage systems: schema evolution, data contracts, DQ frameworks, idempotent backfills, lineage, time-travel, data reproducibility and pipeline observability. - Drive architectural decisions in our GCP / BigQuery / dbt stack — build vs. buy, what to standardize, what to deprecate — and write the design docs that align Engineering, DS, and Product teams. - Raise the engineering bar through code review, design review, and mentorship; level up Senior engineers and unblock the team on the hardest problems. - Partner with data science to translate fuzzy modeling and research needs into pipeline contracts and SLAs that downstream teams can trust. - Own incident response and post-mortems for critical pipeline failures; turn one-off fires into systemic fixes. - Drive design and implementation of AI (Agentic) workflows for data quality and analytics - Influence the broader engineering org's data strategy. Qualifications - 10+ years of software engineering experience, with at least 4 years building production data platforms at meaningful scale (terabytes/day, hundreds of pipelines, or comparable). - Track record of Staff-level technical leadership: setting direction across multiple workstreams, writing design docs others build from, and mentoring senior engineers. - Deep expertise in Python and SQL/dbt, with strong fluency in a modern orchestrator (Dagster, Airflow, Temporal, etc) and a cloud data warehouse (BigQuery, Snowflake, etc). - Demonstrated ownership of a non-trivial data platform — schema design, schema evolution, data quality, lineage, cost, and reliability — not just writing pipelines, but designing the system the pipelines live in. - Strong product judgment — comfortable working with DS, ML, or analytics consumers and translating their needs into clean data contracts. - Excellent written and verbal communication; able to defend technical decisions to engineering, product, and exec stakeholders. Bonus Points - Background contributing to or maintaining open-source data tooling/frameworks (Apache Spark, Apache Beam, Apache Iceberg). - Experience building AI Agents in a data platform setting. What We Offer: We’re a high-performance, low-ego team operating in a fast-moving environment. We care deeply about our customers and expect everyone to take full ownership of their work — this is a place where high expectations fuel even higher growth. If you thrive in ambiguity, take pride in raising the bar, and want to work alongside top-tier peers who challenge and support you, you'll find unma…