Staff Backend Engineer: Data Platform & Lakehouse

Haus Analytics; Seattle (WA); On-site; USD 210,000 - 260,000; Full time

Benefits offered by this job

Flexible PTO

Equity

Health insurance

WFH stipend

Events & Offsites

Free Lunch

New Parent Leave

Job summary

Haus Analytics is seeking a senior backend and data engineering leader to design and scale the data platform. You will own ingestion, normalization, data contracts, and the lakehouse architecture.

You’ll work across GCP, BigQuery, and dbt, mentoring engineers and aligning with product and data science teams. You’ll tackle distributed systems with exactly-once semantics, schema evolution, and cost/performance optimization for large-scale ad data.

Qualifications

• 8+ years of software engineering experience with backend and data focus (senior)
• Hands-on experience with cloud data warehouses or lakehouses (BigQuery preferred)
• Expert-level Python for building services
• Deep SQL/dbt experience and data-model design
• Staff-level technical leadership track record
• Excellent written and verbal communication

Responsibilities

• Architect and build backend services powering Haus's data platform
• Solve distributed-systems challenges in a data context
• Own lakehouse/warehouse as a product: schema, dbt architecture, data quality, lineage
• Set engineering bar: testing, API design, code reviews, observability, CI/CD
• Drive architectural decisions across GCP/BigQuery/dbt/Python stack
• Mentor senior engineers and influence data strategy

Skills

Backend engineering

Data engineering

Python

SQL/dbt

Staff-level leadership

Communication

Tools

BigQuery

dbt

Snowflake

Databricks

Airflow

Job description

Haus Analytics is seeking a senior backend and data engineering leader to design and scale the data platform. You will own ingestion, normalization, data contracts, and the lakehouse architecture.

You’ll work across GCP, BigQuery, and dbt, mentoring engineers and aligning with product and data science teams. You’ll tackle distributed systems with exactly-once semantics, schema evolution, and cost/performance optimization for large-scale ad data.