Staff Backend Engineer - Data Platform- San Francisco

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 on the outputs.

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

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  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.

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  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.

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  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.

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  Drive architectural decisions across our GCP / BigQuery / dbt / Python stack and drive alignment with downstream engineering and data science teams.

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  Mentor senior engineers and influence the broader org's data strategy.

• 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'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.

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

We interview for both halves of this role, strong backend + data experience. Candidates who are strong in only one half typically don't advance