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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 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.
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.
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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.
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.
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.
We interview for both halves of this role, strong backend + data experience. Candidates who are strong in only one half typically don't advance
Contributions to open-source data frameworks or tooling (Apache Spark, Beam, Iceberg, Arrow, or similar).
Haus is a software company providing AI-powered causal marketing measurement and incrementality tools to enterprise brands.
Visit company websiteJobs and hiring trendsUSD 240000-260000 yearly / year
Full-time
Senior · 10+ years experience
Hybrid
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