Staff Data Engineer

Role overview Establish and own the core data engineering capabilities, building on existing infrastructure as the organization brings this work in-house. You’ll connect engineering and business operations by making data reliable, well-documented, and useful for analysts, data scientists, and product leaders.

Responsibilities - Operate and evolve the data warehouse, ingestion pipelines, and transformation layer. - Build reliable pipelines that bring together platform and third-party data for downstream use. - Partner with analysts and data scientists to provide clean, documented datasets and improve query performance. - Assess existing systems, prioritize improvements, and modernize them incrementally. - Introduce data quality checks, monitoring, alerting, and documentation practices that build trust in shared data.

Requirements - At least four years of professional data engineering experience, ideally in a fast-moving organization. - Hands-on familiarity with a modern data stack, including BigQuery or a comparable warehouse, dbt, and Dagster, Airflow, or a similar orchestrator. - Strong SQL skills and fluency in Go, Python, or another common data engineering language. - Experience improving or modernizing inherited data systems in manageable steps. - Clear communication and collaboration across technical and business-focused teams.

Nice to have - Experience moving data infrastructure from outsourced support to an in-house team. - Familiarity with data observability tools. - Experience supporting data science work, including machine learning feature pipelines.

Benefits and work setup - Fully remote, with travel to Los Angeles generally around quarterly and for team gatherings. - Base salary range of $174,000–$240,000, plus equity. - Medical and dental premiums covered for employees and families; paid vacation and holidays; a health membership; and a 401(k) with optional HSA/FSA. - Annual internet and education stipends, plus student loan repayment assistance.