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Haus is a causal marketing platform that helps top businesses optimize billions in ad spend worldwide using AI-driven technology and insights from PhD economists and data scientists. Customers include Dyson, Wayfair, Sonos, FanDuel, SharkNinja, and Intuit.
This is a dual-depth Staff role combining backend systems engineering and data engineering. You will design and lead the data platform that powers Haus's entire incrementality platform—every causal experiment, marketing mix model, and ad-spend optimization decision runs on systems this team builds.
Key Responsibilities:
- Architect and build backend services powering the data platform: high-throughput ingestion from third-party ad-network APIs, customer warehouses, and partner tools; normalization and validation layers; orchestration and observability infrastructure.
- 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 optimization of BigQuery workloads.
- Set the engineering bar for the team: testing strategy, API design, code review, observability, and CI/CD standards.
- Drive architectural decisions across the GCP/BigQuery/dbt/Python stack and align with downstream engineering and data science teams.
- Mentor senior engineers and influence the broader organization's data strategy.
The role requires leading technical direction, contributing hands-on code, and partnering with engineering and product leadership. You will be the engineer the team pulls in on the hardest problems, writing design docs that others build from and defending technical decisions to engineering, product, and executive stakeholders.
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 acceptable).
- Expert-level Python experience for building production services, not just scripts or notebooks.
- Deep SQL/dbt experience: ability to design schemas that survive evolution and 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 relies on for the hardest problems.
- Excellent written and verbal communication; ability to defend technical decisions to engineering, product, and executive stakeholders.
- Ideal candidates are equally strong at backend engineering (production services, APIs, distributed systems) and data platform work, and can review both service PRs and dbt PRs to the same standard.
- Bonus: contributions to open-source data frameworks or tooling (Apache Spark, Beam, Iceberg, Arrow, or similar).
Not a fit if your experience is primarily SQL/dbt transformations, BI, or analytics engineering without significant backend service development; if you've only operated data tools as a user without designing production systems; or if you're a strong backend engineer who views warehouse and data-model work as someone else's job.
