Make smarter enterprise marketing investments with Haus’ AI-powered causal marketing platform and expert guidance. Backed by Insight.
About 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.
What they're looking for
- 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
More about this role
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.
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...
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