Senior Software 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!

What you'll do

This is a unique opportunity to help us build robust and scalable systems that handle data workloads at Haus. The Data Platform includes systems that power the Haus product and are at the heart of what we do.

The ideal candidate is somebody who loves working with data, is a great software engineer, and an excellent communicator. You will design, scale, and maintain the systems that support the Haus platform and enable data-driven decision-making at Haus.

Responsibilities

• Build data solutions on top of Google Cloud Platform using Python and other languages as appropriate.
• Contribute to the technical direction and development of Haus’ data infrastructure, tools, and applications.
• Create and maintain data pipelines and foundational datasets to support analytics, modeling, experimentation, and product / business needs.
• Work closely with science, machine learning, and engineering teams to support the development of new data pipelines.
• Develop and support data quality checks at scale, implementing alerting and anomaly detection as necessary.
• Create scalable dashboards and reports to support business objectives and enable data-driven decision making.
• Partner with data scientists, engineers, and product teams to accomplish all of the above!

Qualifications

Bonus points

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 :

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.

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