Data Engineer

Join WorkOS as a Data Engineer and play a crucial role in the company's operations by ensuring accurate and accessible information and insights. You will own and evolve the internal data platform, including ingestion, orchestration, and access governance, as well as the dbt models and metric definitions. This is a high-ownership role on a lean team, where you will collaborate with various departments and have the opportunity to experiment and find scalable solutions to ambiguous problems.

• Own the reliability, freshness, and scaling of the ingestion and orchestration pipelines that land source data in Snowflake, including monitoring, alerting, runbooks, and backfill and reprocessing patterns.
• Design, build, and scale the core dbt models (Bronze, Silver, Gold) for billing and usage, CRM, product events, and GTM funnel reporting.
• Partner with Product, Finance, RevOps, and GTM to define metrics and codify them in the warehouse, and diagnose and resolve data quality and freshness issues at source, not just downstream.

• A systems thinker who reasons carefully about freshness, correctness, and failure modes, especially for data that everything else depends on
• Experience running data systems in production: monitoring, alerting, debugging, incident response habits, and pragmatic SLO/SLA thinking
• Deep experience with Snowflake, dbt, and an orchestrator such as Prefect, Airflow, or Dagster, and with ingesting data from production databases and SaaS sources, including backfill and reprocessing patterns
• Experience with warehouse access governance: RBAC, masking policies, and PII handling
• Working knowledge of how GTM, Finance, and Product teams operate in addition to working with Engineering, and experience translating ambiguous business questions into technical specs
• Proven experience operating as the only engineer on a layer, balancing foundational architecture with urgent business needs
• Strong SQL and Python; disciplined engineering practices (tests, docs, reviews, CI/CD)
• Comfortable using LLMs and coding agents as part of your day-to-day development workflow, with the judgment to validate their output and scale it into shared processes, and embracing AI and automation to scale platform work and accelerate your teammates
• Strong data modeling judgment and an understanding of schema evolution
• 5+ years (or equivalent) building and operating production data platforms, including the transformation layer
• Pragmatic: you start with the simplest solution that works, prove it, then scale it, and you balance fast answers with durable solutions
• You look for where AI can remove a bottleneck for the people and agents who depend on the warehouse, and you build the durable tools and workflows they rely on every day