Staff Engineer - Data Platform - San Francisco

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.

- 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

- 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.

- Background contributing to or maintaining open-source data tooling/frameworks (Apache Spark, Apache Beam, Apache Iceberg).