Data Engineer at WorkOS

WorkOS builds modern developer tools and APIs that make it easy for companies to become Enterprise Ready. Their platform powers authentication, identity, authorization, and other critical infrastructure for developers to securely scale products to large organizations. Recently valued at $2B after a $100M Series C funding round, WorkOS serves enterprise features for AI companies like OpenAI, Cursor, Perplexity, Sierra, and Plaid.

About the Role

The Data team at WorkOS ensures accurate and accessible information and insights across the company, from visualization tools to AI agent responses. They own the internal data platform end-to-end, including ingestion, orchestration, Snowflake warehouse, dbt transformations, governance, and access controls. The team also manages the consumption layer: reverse-ETL syncs to Salesforce and Slack, semantic views for AI agents, consistent reporting definitions, and visualization tooling like Flashboards.

The team operates on an agent-first system, moving fast, documenting processes, and leveraging AI agents for tasks like warehouse queries, runbooks, and pull requests. Collaboration, transparency, and rapid experimentation are key.

WorkOS is hiring a Data Engineer to own and evolve systems that move, transform, protect, and serve data across their internal warehouse. This is a high-ownership role on a lean team, requiring direct partnerships with Product Engineering, RevOps, Finance, GTM Engineering, and Security. The role spans ingestion, orchestration, access governance, dbt models, metric definitions, and AI applications across the data stack.

The ideal candidate doesn’t need prior experience in this exact role. The best data engineers at WorkOS are proactive, detail-oriented, and collaborative. They fix issues at their source, automate runbooks, and enjoy solving ambiguous problems with scalable solutions.

Responsibilities

• Own the reliability, freshness, and scaling of ingestion and orchestration pipelines landing source data in Snowflake, including monitoring, alerting, runbooks, and backfill/reprocessing patterns.
• Design, build, and scale core dbt models (Bronze, Silver, Gold) for billing, usage, CRM, product events, and GTM funnel reporting.
• Partner with Product, Finance, RevOps, and GTM to define metrics, codify them in the warehouse, and diagnose data quality/freshness issues.
• Manage Snowflake RBAC, dynamic masking, and PII classification for humans, agents, and service accounts, ensuring sensitive data protection without blocking legitimate use.
• Own the reverse-ETL layer and runbooks delivering warehouse data to Salesforce, Slack, and internal agents.
• Extend semantic views and context for AI agents to accurately answer business questions and expand their operational capabilities.
• Build and advance CI/CD review gates in the data-platform monorepo, including automated reviews for agent-authored pull requests.
• Own the infrastructure for the data platform, including compute, deployments, secrets, access patterns, and environments for ingestion/orchestration, with attention to availability and failure modes.

Example projects include:

• Standardizing Postgres and SaaS source ingestion and consolidating pipeline deployment/orchestration in Prefect.
• Managing Snowflake RBAC, resource management, and masking policies as code with Terraform.
• Using query history to improve analytics tables and semantic views for AI agents.
• Automating masking coverage for new data sources to reduce human intervention.
• Automating near-certain matches in the Identity Graph.
• Pseudonymizing product data in the ingestion path alongside compliance and data-deletion policies.
• Extending transcript aggregation across sources with PII detection before warehouse entry.
• Building reverse-ETL frameworks and runbooks for data delivery to other tools/agents.
• Running self-hosted data services on Kubernetes and managing AWS resources (IAM, storage, secrets) as code with Terraform.

Qualifications

• 5+ years building and operating production data platforms, including transformation layers.
• Deep experience with Snowflake, dbt, and orchestrators like Prefect, Airflow, or Dagster, including data ingestion from production databases and SaaS sources.
• Experience running data systems in production, with monitoring, alerting, debugging, incident response, and SLO/SLA thinking.
• Experience with warehouse access governance, including RBAC, masking policies, and PII handling.
• Strong data modeling judgment and understanding of schema evolution.
• Strong SQL and Python skills, with 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 team operations, and experience translating ambiguous business questions into technical specs.
• Comfortable using LLMs and coding agents in daily workflows, with judgment to validate their output and scale into shared processes.
• Systems thinker focused on freshness, correctness, and failure modes, especially for critical data.
• Pragmatic approach: start with simple solutions, prove them, then scale, balancing fast answers with durable solutions.
• Looks for AI solutions to remove bottlenecks for people and agents who depend on the warehouse, building durable tools and workflows.