Staff Engineer - Data Platform

About Haus Haus is the incrementality platform leading brands trust to optimize billions in ad spend worldwide. Using frontier causal inference-based econometric models to run experiments, we help brands measure the business impact of marketing, pricing, and promotions with scientific precision. Over $360B is spent annually on paid advertising in the US alone, and the famous quote “half the money I spend on advertising is wasted; the trouble is I don't know which half” still rings true. Haus helps marketers identify which half, and reallocate it to maximize growth. With a founding team of former product managers, economists, and engineers from Google, Netflix, Meta, and Amazon, we make high-quality decision science, incrementality testing, and causal marketing mix modeling accessible to businesses of all sizes—automating the heavy lifting of experiment design, data processing, and insights generation. Haus works with leading brands like FanDuel, Sonos, and Dr. Squatch, delivering ROI gains as high as 30x. Haus is well-capitalized and backed by top-tier VCs, including Insight Partners, Baseline Ventures, Haystack, and others. We're honored that Haus has once again been recognized by LinkedIn as a 2025 Top Startup https://www.linkedin.com/pulse/linkedin-top-startups-2025-50-us-companies-rise-linkedin-news-hox6f/! 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 - 8-10+ 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 th