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 RoleThis backend role is centered on developing workflows to help customers connect their data and to configure the ingestion of that data with little to no human intervention. You will be a key part of the onboarding experience for customers as well as Haus’s efforts to scale and efficiently service an ever-expanding customer base.
The Data Onboarding team is part of Haus's Data Platform, which 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 whose models must be correct, because our customers make million-dollar decisions on the outputs.
What you’ll doJoin the team responsible for accessing, ingesting, and validating customer data
Build, maintain and extend long-running workflows, APIs, data pipelines, customer-facing UIs, and scripts used in automatic and manual data ingestion flow
Contribute to AI (MCP) services owned by the team
Partner with Customer Success teams to build custom data models for customers
Participate in an on-call rotation
4+ years experience building backend services & APIs (backend Engineer, full stack engineer, data engineer or similar)
Proficiency in Python and SQL/dbt, with strong fluency in a modern orchestrator (Dagster, Airflow, Temporal, etc)
Experience with cloud data lakehouse/warehouse (BigQuery, Databricks, etc).
Experience with cloud infrastructure: Google Cloud, AWS
Experience with React and/or other frontend frameworks
Experience with Fivetran/Airbyte and Terraform
Experience integrating with commerce platforms: Shopify, Amazon Seller / Vendor
Earlier stage startup experience
You're passionate about automating data ingestion and processing workflows
You're equally strong at backend engineering: production services, APIs, distributed systems.
You are passionate about customer experience and think deeply about solving customer pain points in an efficient and scalable way
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.
Skills Required
- 4+ years of experience building backend services and APIs, including backend engineering, full-stack engineering, data engineering, or similar experience.
- Proficiency in Python.
- Proficiency in SQL and dbt.
- Strong fluency with a modern workflow orchestrator such as Dagster, Airflow, or Temporal.
- Experience with cloud data lakehouse or warehouse technologies such as BigQuery or Databricks.
- Experience with cloud infrastructure, including Google Cloud or AWS.
- Experience with React or other frontend frameworks.
- Experience with Fivetran, Airbyte, and Terraform.
- Experience integrating with commerce platforms such as Shopify, Amazon Seller, or Amazon Vendor.
- Experience at an earlier-stage startup.
Haus.io Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Haus.io and has not been reviewed or approved by Haus.io.
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Healthcare Strength — Healthcare includes medical, dental, and vision with multiple plan options and is described as “top of the line,” signaling robust core coverage for a startup.
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Leave & Time Off Breadth — Flexible PTO and “all the classics, plus some” holidays indicate generous time‑away norms that support rest and recharge.
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Equity Value & Accessibility — Equity grants are explicitly offered, positioning employees as owners and aligning rewards with company outcomes.
Haus.io Insights
What We Do
Haus is a decision science platform built on your own data. Our products combine state-of-the-art causal inference and econometrics to help brands make informed investment decisions.






