Data Engineer

WorkOS•San Francisco, CA

2d•$175,000 - $240,000•Remote

About The Position

The Data team at WorkOS plays a central role on the company operations to ensure information and insights are accurate and available from any surface, whether that's a visualization tool or an agent's response, to accelerate company growth and scale. The team owns WorkOS's internal data platform end to end: ingestion, orchestration, the Snowflake warehouse, dbt transformations, governance and access controls. We also own the consumption layer: the reverse-ETL syncs that land data in Salesforce and Slack, the semantic views that agents query, the definitions that keep reporting consistent across the company, and the visualization tooling the company uses (ask us about Flashboards). We run on an agent-first operating system: we are a lean team that moves fast, we document so agents can execute, and AI agents query the warehouse, run our runbooks, and open pull requests alongside us. We trust each other's expertise, we work out loud and in the open, and not opposed to rapid experimentation to get to the right solution for the business. 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 role spans the platform and what runs on it. You will own ingestion, orchestration, and access governance; the dbt models and metric definitions that depend on them; and how AI is applied across the data stack, so that trusted data is easy for both people and AI agents to discover, understand, and work with.

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, and with 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, and embracing AI and automation to scale platform work and accelerate your teammates
A systems thinker who reasons carefully about freshness, correctness, and failure modes, especially for data that everything else depends on
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

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, so sensitive data is protected without blocking legitimate use, 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, from runbooks and ingestion to pull requests
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: the compute, deployments, secrets and access patterns, and environments behind ingestion and orchestration, with attention to availability and failure modes, in partnership with our infrastructure engineers

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
Monthly stipend for gyms, yoga classes, race registrations or whatever keeps you active
Monthly stipend for a massage, meditations class, therapy, or activities that enhance your well-being
Commuter benefits for hybrid employees in SF/NYC
Unlimited token usage!