---
title: 'Staff Backend Engineer - Data Platform- Seattle at Haus Analytics'
canonical: 'https://feeny.ai/job/staff-backend-engineer-data-platform-seattle-haus-analytics-seattle-vastxze0k4hz'
type: 'job'
last_seen: '2026-09-06'
---

# Staff Backend Engineer - Data Platform- Seattle at Haus Analytics

- **Company:** Haus Analytics
- **Location:** Seattle, WA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-24
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/haus/f3df4fd7-46ec-4c27-92b5-82f35d925cc1

## Job description

## 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 BigQuery whose models must be correct, because our customers make million-dollar decisions on the outputs.

You will lead the team, setting technical direction, contributing hands-on and partnering with engineering and product leaders.

## 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

- 8+ 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).
- Expert-level Python experience for building services, not just scripts or notebooks.
- Deep SQL/dbt experience: you can design schemas that survive evolution, reason about correctness and performance of complex analytical queries.
- 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.

## BONUS POINTS

- Contributions to open-source data frameworks or tooling (Apache Spark, Beam, Iceberg, Arrow, or similar).

## 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

## 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.

## About Haus Analytics

## Company Overview
- **One-liner**: Haus Analytics provides an AI-powered causal marketing measurement platform that helps brands measure the true incremental impact of their ad spend through experiments and daily attribution.
- **Entity Type**: Private (startup, latest funding round: additional $20M in 2024; total raised $57.15M)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2021
- **Founders**: Zach Epstein (CEO)

## Core Business
- **Industry**: Marketing analytics / Advertising technology / Decision science
- **Target Customers**: B2B – enterprise and growth-stage brands including Intuit, Hims & Hers, Pernod Ricard USA, Coursera, Bally Sports, Caraway, and Sonos
- **Mission**: “Democratize access to world-class decision science tools” and transform how businesses make marketing investment decisions.

## Products & Services
- **GeoLift**: Self-service incrementality testing platform that runs scientifically sound test/control geo experiments to measure the causal impact of advertising.
- **Causal Attribution**: Daily incrementality reporting that de‑biases legacy attribution tools and provides daily insights on the incremental impact of marketing across channels and tactics.
- **Haus Copilot**: AI‑powered assistant that designs, optimizes, and analyzes experiments from hypothesis through post‑treatment window.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed
- **Key Metric**: Total funding raised $57.15M (Series unknown, latest $20M closed in mid‑2024)
- **Notable Investors & Partners**: 01 Advisors (David Fischer, Dick Costolo, Adam Bain), Insight Partners, DST Global, Gokul Rajaram, Baseline Ventures, Haystack Ventures, Octave, Mantis Venture Capital
- **Growth Signals**:
  - Customers collectively represent over $1 trillion in combined revenue.
  - Haus has helped optimize more than $30 billion in ad spend.
  - Runs over 4,000 experiments per year.
  - One customer (Newton Living) reported a >10x ROI on their annual Haus investment in the first two months.

## Competitive Advantages
- Combines rigorous causal inference (econometrics, statistics, AI) with an easy‑to‑use SaaS platform.
- Privacy‑durable methodology that works in a cookieless, IDFA‑limited world.
- GeoLift and Causal Attribution are patent‑pended or proprietary approaches that few competitors offer at scale.
- Team includes former leaders from Google, Netflix, Amazon, and other top tech companies.

## Strategic Focus
- Deepen self‑service experimentation and daily causal attribution capabilities.
- Expand into adjacent industries beyond advertising (e.g., pricing, product decisions) and broader decision science.
- Continue to build AI‑powered Copilot features to reduce the need for dedicated data science teams.

## Why Work Here
- **Culture**: “Experiment boldly,” “Done is better than perfect,” “Call it what it is” – values that encourage risk‑taking, candor, and speed.
- **Work Model**: Hub‑centered hybrid with offices in San Francisco, New York, and Seattle. Many roles are hybrid or location‑anchored; remote employees get regular team onsites and travel support.
- **Notable Perks**: Equity, healthcare (medical, dental, vision), flexible PTO, new parent leave, WFH stipend, free in‑office lunch, brand discounts, events and offsites.
- **Engineering & Science Culture**: Built by scientists and engineers; the team applies economics, ML, and data pipeline expertise to solve hard marketing measurement problems.

## Sources
1. [haus.io – Homepage](https://www.haus.io/)
2. [haus.io – About / Leadership](https://www.haus.io/about)
3. [haus.io – Careers](https://www.haus.io/careers)
4. [cbinsights.com – Haus Analytics Profile](https://www.cbinsights.com/company/haus-analytics)
5. [ashbyhq.com – Haus Careers](https://jobs.ashbyhq.com/haus)

## Other roles at Haus Analytics

- [Senior Software Engineer - Data Onboarding - Seattle](https://feeny.ai/job/senior-software-engineer-data-onboarding-seattle-haus-analytics-seattle-dg5dfwnrxprj) — Seattle, WA
- [Senior Software Engineer - Data Onboarding - San Francisco](https://feeny.ai/job/senior-software-engineer-data-onboarding-san-francisco-haus-analytics-san-e01842gf9cf3) — San Francisco, CA
- [Director of Demand Generation](https://feeny.ai/job/director-of-demand-generation-haus-analytics-remote-qkzh9vrhyx7s)
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- [Staff Machine Learning Engineer - San Francisco](https://feeny.ai/job/staff-machine-learning-engineer-san-francisco-haus-analytics-san-francisco-12v9ktej0ey5) — San Francisco, CA
- [Staff Machine Learning Engineer - New York](https://feeny.ai/job/staff-machine-learning-engineer-new-york-haus-analytics-new-york-98726n18ebs1) — New York, NY
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