Staff Data Engineer (Snowflake/dbt)

Location: Remote - US

Salary: $161,500–$209,000 annually for Tier 1 (US, Canada), plus 0.017%–0.023% equity. Compensation ranges also include $112,500–$131,000 for Chile, $104,225–$121,193 for Brazil and Colombia, and $78,000–$91,000 for the Dominican Republic.

Company

hirify.global supports aesthetic entrepreneurs in building profitable, independent practices and provides data products for more than 700 practices.

What you will do

• Own hirify.global's data infrastructure end to end, including Prefect, Airbyte, Snowflake Openflow, Snowflake, dbt, and Omni.
• Partner with Engineering on production schema changes from Postgres to Snowflake and with Finance on accounting logic.
• Build and maintain pipelines, models, alerts, lineage, documentation, and reliable access to business-critical data.
• Ship a first-quarter high-impact project: an accrual-basis accounting model or historical EMR modeling aligned with existing practice KPIs.
• Establish SOPs, KPIs, security reviews, schema-change processes, failure triage, and infrastructure performance and cost monitoring.
• Optimize the stack and build pipelines and models that advance Practice Intelligence from descriptive to predictive and prescriptive, including safeguards for LLM-generated content.

Requirements

• 5–10 years of data engineering experience, including at least one period as an early data hire who developed a system from basic foundations into business-critical infrastructure.
• Deep dbt and SQL modeling experience with complex business logic, including cash-versus-accrual accounting, incentive calculations, revenue definitions, and downstream metric impact analysis.
• Strong Python and software engineering fundamentals, with production experience using Prefect, Airflow, Dagster, or a similar orchestration platform.
• Experience handling schema evolution from an operational database into a warehouse and establishing alerting and observability.
• Experience leading end-to-end delivery: gathering requirements from Finance, Product, and Sales, scoping work, making architectural decisions, shipping solutions, and explaining tradeoffs and business value.
• Ownership of uptime, reliability, cost, SLAs, runbooks, KPIs, and cost optimization for data infrastructure.
• Fluency with AI tooling such as Claude Code, Cursor, n8n, or similar, plus a perspective on appropriate LLM use and required guardrails in a data stack.
• Ability to treat data teams as drivers of business outcomes rather than ticket-processing services.

Nice to have

• No optional qualifications are explicitly listed.

Culture & Benefits

• Remote-first work environment with a small, ambitious data team.
• Highly visible individual contributor role as the second internal data hire, with input into the data roadmap and engineering standards.
• Modern stack including Snowflake, dbt, Omni, Airbyte, Prefect, GitHub, and integrated AI tooling.
• Close partnership with Product and Engineering and exposure to C-Suite-level initiatives.
• Values include ownership, generosity, speed with care, simplification, and continuous learning.
• Inclusive workplace with interview accommodations available on request.