About the role
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
This is a dual-depth role: backend systems engineering + data engineering. You'll design the services and pipelines that ingest data at scale and the lakehouse/warehouse models that make it trustworthy and reproducible.
Haus's Data Platform powers the entire incrementality platform: every causal experiment, every marketing mix model, every dollar of ad spend we help customers reallocate runs on systems this team builds. Under the hood, that platform is a set of distributed backend services — ingestion from dozens of ad-network APIs, customer warehouses, and partner tools; normalization and validation layers; orchestration and observability infrastructure — feeding a BigQuery + dbt warehouse whose models must be correct, because our customers make million-dollar decisions on the outputs.
You will be the senior-most IC on a 6–10 person team, setting technical direction and partnering directly with engineering leadership, product engineering and data science.
What you’ll do
Architect and build the backend services that power Haus's data platform: high-throughput ingestion from third-party APIs, normalization services, data contracts, and the control plane that orchestrates it all.
Solve hard distributed-systems problems in a data context: exactly-once semantics, idempotent reprocessing and backfills, schema evolution without downtime, graceful handling of flaky third-party APIs at scale.
Own the lakehouse/warehouse as a product: schema and data-model design, dbt architecture, data quality frameworks, lineage, and cost/performance of BigQuery workloads.
Set the engineering bar for the team — testing strategy, API design, code review, observability, CI/CD.
Drive architectural decisions across our GCP / BigQuery / dbt / Python stack and drive alignment with downstream engineering and data science teams.
Mentor senior engineers and influence the broader org's data strategy.
Qualifications
10+ years of software engineering experience, with deep backend and data expertise.
Solid, hands-on experience with a cloud data warehouse or lakehouse (BigQuery preferred; Snowflake, Databricks, or Iceberg-based stacks).
Deep SQL/dbt experience: you can design schemas that survive evolution, reason about correctness and performance of complex analytical queries.
Expert-level Python experience for building services, not just scripts or notebooks.
Track record of Staff-level technical leadership: setting direction across multiple workstreams, writing design docs others build from, and being the engineer the team pulls in on the hardest problems.
Excellent written and verbal communication; able to defend technical decisions to engineering, product, and exec stakeholders.
You might be a great fit if
You're passionate about data — pipelines, lakehouses, warehouses, the craft of making data trustworthy at scale.
You're equally strong at backend engineering: production services, APIs, distributed systems.
You're the engineer who reviews both the service PR and the dbt PR, and holds them to the same standard.
This role is probably not for you if
Your experience is primarily SQL/dbt transformations, BI, or analytics engineering without significant backend service development.
You've operated data tools (Airflow, Fivetran, dbt) as a user, but haven't designed and written the production systems underneath them.
You're a strong backend engineer who sees warehouse and data-model work as someone else's job.
Interview process (what we test for)
We interview for both halves of this role, strong backend + data experience. Candidates who are strong in only one half typically don't advance
Bonus points
Contributions to open-source data frameworks or tooling (Apache Spark, Beam, Iceberg, Arrow, or similar).
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 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. We make hiring decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status.
We believe diverse perspectives make us stronger and are committed to an inclusive culture where everyone feels seen, heard, and empowered to contribute. Bring your authentic self — we would love to hear from you.