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WorkOS is a $2B Series C-funded developer platform that provides enterprise-ready authentication, SSO, directory sync, and identity infrastructure. The company powers critical authentication and access control for leading AI companies including OpenAI, Cursor, and Perplexity.
The Data team owns WorkOS's internal data platform end-to-end: ingestion, orchestration, Snowflake warehouse, dbt transformations, governance, and access controls. The team also manages the consumption layer including reverse-ETL syncs to Salesforce and Slack, semantic views for agent queries, metric definitions, and visualization tooling. The team operates on an agent-first operating system with a lean structure that moves fast, documents thoroughly, and leverages AI agents to query the warehouse, run runbooks, and contribute pull requests.
As a Data Engineer, you will own and evolve the systems that move, transform, protect, and serve data across the internal warehouse. This is a high-ownership role on a lean team where you will be the DRI (directly responsible individual) for the platform and its core data models. You will partner directly with Product Engineering, RevOps, Finance, GTM Engineering, and Security to raise the bar on reliability, correctness, and operational rigor.
Key responsibilities include:
- Own reliability, freshness, and scaling of ingestion and orchestration pipelines landing data in Snowflake, including monitoring, alerting, runbooks, and backfill patterns
- Design and scale core dbt models (Bronze, Silver, Gold) for billing, usage, CRM, product events, and GTM reporting
- Partner with business teams to define metrics, codify them in the warehouse, and resolve data quality issues at source
- Own Snowflake RBAC, dynamic masking, and PII classification for humans, agents, and service accounts
- Own reverse-ETL layer and runbooks delivering data to Salesforce, Slack, and internal agents
- Extend semantic views and evaluations enabling agents to answer business questions accurately
- Build and advance CI/CD review gates in the data-platform monorepo, including automated review for agent-authored pull requests
- Own infrastructure for the data platform: compute, deployments, secrets, access patterns, and environments
Example projects include standardizing Postgres and SaaS ingestion with Prefect, managing Snowflake RBAC and masking policies as code with Terraform, improving analytics tables using query history, automating masking coverage for new data sources, automating identity graph matching, pseudonymizing product data, extending transcript aggregation with PII detection, building reverse-ETL frameworks, and running self-hosted data services on Kubernetes with AWS resource management.
The ideal candidate notices when numbers don't match, traces issues from dashboard through models to connectors, fixes problems end-to-end, and automates repetitive work. You enjoy collaboration across business functions and find simple, scalable solutions to ambiguous problems.
REQUIREMENTS:
- 5+ years building and operating production data platforms, including transformation layer
- Deep experience with Snowflake, dbt, and an orchestrator (Prefect, Airflow, or Dagster); experience ingesting from production databases and SaaS sources with backfill/reprocessing patterns
- Production data systems experience: monitoring, alerting, debugging, incident response, pragmatic SLO/SLA thinking
- Warehouse access governance experience: RBAC, masking policies, PII handling
- Strong data modeling judgment and schema evolution understanding
- Strong SQL and Python; disciplined engineering practices (tests, docs, reviews, CI/CD)
- Proven experience operating as the sole engineer on a layer, balancing foundational architecture with urgent business needs
- Working knowledge of GTM, Finance, and Product operations; ability to translate ambiguous business questions into technical specs
- Comfortable using LLMs and coding agents in daily development, with judgment to validate and scale their output
- Systems thinker reasoning carefully about freshness, correctness, and failure modes
- Pragmatic approach: start with simplest working solution, prove it, then scale; balance fast answers with durable solutions
- Ability to identify where AI can remove bottlenecks and build durable tools and workflows for people and agents
About WorkOS
SaaS / Enterprise Software — developer platform for enterprise-ready authentication, SSO, and directory sync.
