Staff Data Engineer

Role overview Own the data platform behind business reporting and customer-facing analytics at a growing, remote-first software company. As a senior individual contributor and an early member of the data team, you’ll strengthen pipelines, warehouse models, and data quality practices so teams can trust key metrics. The work combines hands-on engineering with technical leadership and close partnership across Engineering, Finance, Product, and other business teams.

Responsibilities - Take ownership of data pipelines, warehouse models, orchestration, monitoring, documentation, and lineage. - Maintain and evolve dbt models for financial and operational reporting, tracing how upstream changes affect downstream metrics. - Partner with Engineering on production database changes and reliable movement of data into the warehouse. - Deliver a major data initiative, such as supporting both cash and accrual reporting or integrating historical clinical records with existing business metrics. - Establish repeatable practices for schema changes, incident triage, security reviews, service expectations, and infrastructure cost tracking. - Improve refresh speed and reliability, and develop data products that can progress from descriptive reporting toward predictive insights, with safeguards for AI-generated content.

Requirements - Five to ten years of data engineering experience, including experience building a data system into a business-critical service. - Deep SQL and dbt skills, with experience modeling complex business or financial concepts and explaining their effects on reported metrics. - Strong Python and software engineering fundamentals, plus experience operating production data pipelines with an orchestration tool. - Experience handling schema evolution between operational systems and a warehouse, and building useful alerting and observability. - Proven ability to lead work end to end: gather needs from nontechnical partners, scope and design a solution, deliver it, and explain its tradeoffs and value. - A strong sense of ownership for reliability, service levels, documentation, and cost; practical experience with AI tools and a considered view of their use in data systems.

Benefits and work setup - High-visibility individual contributor role with meaningful input into the data roadmap and engineering practices. - Modern data stack and close collaboration with Product and Engineering; the team is remote-first.