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About the Role

This is a dual-depth role focusing on backend systems engineering and data engineering. You will design the services and pipelines that ingest data at scale, and the lakehouse/warehouse models that ensure data trustworthiness and reproducibility. The Data Platform powers the entire incrementality platform, including causal experiments, marketing mix models, and ad spend reallocation, all running on systems built by this team. You will be the senior-most individual contributor on a 6-10 person team, setting technical direction and collaborating with engineering leadership, product engineering, and data science.

Responsibilities

Architect and build backend services for Haus's data platform, including high-throughput ingestion from third-party APIs, normalization services, data contracts, and orchestration control plane.
Solve complex distributed systems problems in a data context, such as exactly-once semantics, idempotent reprocessing, schema evolution without downtime, and handling flaky third-party APIs.
Own the lakehouse/warehouse as a product, including schema and data-model design, dbt architecture, data quality frameworks, lineage, and cost/performance optimization of BigQuery workloads.
Set the engineering standard for the team, covering testing strategy, API design, code review, observability, and CI/CD.
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.

Requirements

10+ 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).
Deep SQL/dbt experience, including designing schemas for evolution and reasoning about correctness and performance of complex analytical queries.
Expert-level Python experience for building production services.
Track record of Staff-level technical leadership, including setting direction, writing design documents, and solving difficult problems.
Excellent written and verbal communication skills, with the ability to defend technical decisions to various stakeholders.
Contributions to open-source data frameworks or tooling (Apache Spark, Beam, Iceberg, Arrow, or similar) are a plus.

Skills

Backend systems engineering
Data engineering
Distributed systems
Data warehousing
Lakehouse architecture
BigQuery
Snowflake
Databricks
Iceberg
SQL
dbt
Python
API design
Data modeling
Data quality frameworks
Observability
CI/CD

Location

San Francisco
Seattle
New York City

Work Type

Hybrid

Experience Level

Senior-level
Staff-level

Benefits

Flexible PTO
Equity
Health, dental, and vision insurance
WFH stipend
Events & Offsites
Free Lunch (in office locations)
New Parent Leave

About the Company

Haus is the causal marketing platform top businesses trust to optimize billions in ad spend worldwide.
Haus's AI-driven technology translates complex marketing measurement into clear action and outcomes, enabling brands to optimize spend, accelerate growth, and make smarter marketing decisions at scale.
We are a high-performance, low-ego team operating in a fast-moving environment.
We care deeply about our customers and expect everyone to take full ownership of their work.
We work in small, mission-driven teams that prioritize inclusion, collaboration, and growth over hierarchy or red tape.

Equal Opportunity

Haus is an equal opportunity employer. We make hiring decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status.
We believe diverse perspectives make us stronger and are committed to an inclusive culture where everyone feels seen, heard, and empowered to contribute. Bring your authentic self — we would love to hear from you.