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!

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 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:
R&D Roles:
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 unmatched opportunities here. If you're looking for predictability or rigid structure or you prefer order-taking to go-getting, we’re probably not the right fit — and that’s okay.

We work in small, mission-driven teams that prioritize inclusion, collaboration, and growth over hierarchy or red tape.

Some of our benefits include:

• Flexible PTO - take time when you need it!

• Equity – Startup environment with part-ownership in our successes

• Top of the line health, dental, and vision insurance - multiple plan options so you can pick what fits you best

• WFH stipend to support the set up you need to be productive

• Events & Offsites – opportunities to connect and celebrate in real life!

• Free Lunch – Grab a bite on us when you choose to work from the office (hub locations include SF, NYC and Seattle)

• New Parent Leave – take time to welcome your newest Hausmate
  

We value in-person collaboration at Haus and give preference to candidates within commuting distance of our offices in San Francisco, Seattle, and New York City.

Haus is an equal opportunity employer.