Staff Engineer - Data Platform
Staff-level technical lead and architect for Haus's data ingestion and normalization platform. Owns schema evolution, data contracts, DQ, lineage, and observability in a GCP/BigQuery/dbt stack. Partners with DS and Product; mentors senior engineers.
What you’ll do
- Be the tech-lead and architect for Haus's data ingestion and normalization platform — ad network APIs (Google, Meta, TikTok, Amazon, etc.), Fivetran connectors, and customer warehouses (Snowflake, BigQuery) — balancing throughput, cost, and reliability.
- Design and lead implementation of high-leverage systems: schema evolution, data contracts, DQ frameworks, idempotent backfills, lineage, time-travel, data reproducibility and pipeline observability.
- Drive architectural decisions in our GCP / BigQuery / dbt stack — build vs. buy, what to standardize, what to deprecate — and write the design docs that align Engineering, DS, and Product teams.
- Raise the engineering bar through code review, design review, and mentorship; level up Senior engineers and unblock the team on the hardest problems.
- Partner with data science to translate fuzzy modeling and research needs into pipeline contracts and SLAs that downstream teams can trust.
- Own incident response and post-mortems for critical pipeline failures; turn one-off fires into systemic fixes.
- Drive design and implementation of AI (Agentic) workflows for data quality and analytics.
- Influence the broader engineering org's data strategy.
Qualifications
- 8-10+ years of software engineering experience, with at least 4 years building production data platforms at meaningful scale (terabytes/day, hundreds of pipelines, or comparable).
- Track record of Staff-level technical leadership: setting direction across multiple workstreams, writing design docs others build from, and mentoring senior engineers.
- Deep expertise in Python and SQL/dbt, with strong fluency in a modern orchestrator (Dagster, Airflow, Temporal, etc) and a cloud data warehouse (BigQuery, Snowflake, etc).
- Demonstrated ownership of a non-trivial data platform — schema design, schema evolution, data quality, lineage, cost, and reliability — not just writing pipelines, but designing the system the pipelines live in.
- Strong product judgment — comfortable working with DS, ML, or analytics consumers and translating their needs into clean data contracts.
- Excellent written and verbal communication; able to defend technical decisions to engineering, product, and exec stakeholders.
Bonus Points
- Background contributing to or maintaining open-source data tooling/frameworks (Apache Spark, Apache Beam, Apache Iceberg).
- Experience building AI Agents in a data platform setting.
What We Offer
- Flexible PTO
- Equity
- Top of the line health, dental, and vision insurance
- WFH stipend
- Events & Offsites
- Free Lunch (SF, NYC, Seattle offices)
- New Parent Leave
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