SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
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-level role combining backend systems engineering and data engineering. You will lead the Data Platform team 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 dozens of 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 through testing strategy, API design, code review, observability, and CI/CD practices.
- Drive architectural decisions across the GCP/BigQuery/dbt/Python stack and ensure alignment with downstream engineering and data science teams.
- Mentor senior engineers and influence the broader organization's data strategy.
- Lead the team, setting technical direction, contributing hands-on, and partnering with engineering and product leaders.
The role requires expertise in both backend service development and data platform design. You'll be the engineer who reviews both service PRs and dbt PRs to the same standard, ensuring production-grade quality across the entire data stack.
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 pulls in on the hardest problems.
- Excellent written and verbal communication; able to defend technical decisions to engineering, product, and executive stakeholders.
- Passion for data—pipelines, lakehouses, warehouses, and the craft of making data trustworthy at scale.
- Equally strong at backend engineering: production services, APIs, distributed systems.
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; you've operated data tools as a user but haven't designed production systems underneath; or you're a strong backend engineer who sees warehouse and data-model work as someone else's job.
