Data Engineer

We're hiring a Data Engineer to own and evolve the systems that move, transform, protect, and serve data across our internal warehouse. This is a high-ownership role on a lean team: you will be the DRI for the platform and its core data models, partner directly with Product Engineering, RevOps, Finance, GTM Engineering, and Security, and raise the bar on reliability, correctness, and operational rigor. The team owns WorkOS's internal data platform end to end: ingestion, orchestration, the Snowflake warehouse, dbt transformations, governance and access controls.

Responsibilities

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; Own Snowflake RBAC, dynamic masking, and PII classification for humans, agents, and service accounts, and review DDL and access requests from Engineering and GTM; Own the reverse-ETL layer and the runbooks that deliver warehouse data to Salesforce, Slack, and internal agents; Extend the semantic views, context, and evaluations that let agents answer business questions accurately, and expand what agents can operate directly; Build and advance the CI/CD review gates in the data-platform monorepo, including the automated review that agent-authored pull requests pass through; Own the infrastructure the data platform runs on, including compute, deployments, secrets and access patterns, and environments behind ingestion and orchestration, in partnership with infrastructure engineers

Requirements

5+ years (or equivalent) building and operating production data platforms, including the transformation layer; Deep experience with Snowflake, dbt, and an orchestrator such as Prefect, Airflow, or Dagster; Experience ingesting data from production databases and SaaS sources, including backfill and reprocessing patterns; Experience running data systems in production: monitoring, alerting, debugging, incident response habits, and pragmatic SLO/SLA thinking; Experience with warehouse access governance: RBAC, masking policies, and PII handling; Strong data modeling judgment and an understanding of schema evolution; Strong SQL and Python; disciplined engineering practices (tests, docs, reviews, CI/CD); Proven experience operating as the only engineer on a layer, balancing foundational architecture with urgent business needs; 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; 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; A systems thinker who reasons carefully about freshness, correctness, and failure modes; 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

Benefits

401k matching; Competitive Equity; Healthcare, dental and vision coverage; FSA, ST/LT Disability, Voluntary Life; Carrot fertility benefits; 20 days paid vacation + 10 holidays + unlimited sick leave; 12 weeks fully paid parental leave; Fitness monthly stipend for gyms, yoga classes, race registrations; Wellness monthly stipend for massage, meditation classes, therapy, or well-being activities; Commuter benefits for hybrid employees in SF/NYC; Unlimited token usage; Parental leave, paid time off and fully remote working arrangements