Data Engineer
$175k - $240k • Remote • United States & Canada • Full-time
Posted 24d ago
About the job
WorkOS is seeking a Data Engineer to join their central Data team, which is responsible for the company's internal data platform from end to end. This includes data ingestion, orchestration, the Snowflake warehouse, dbt transformations, governance, and access controls. The team also manages the consumption layer, including reverse-ETL syncs, semantic views for AI agents, reporting definitions, and visualization tooling. The role involves owning and evolving systems that move, transform, protect, and serve data, acting as the DRI for the platform and its core data models. You will partner with various teams like Product Engineering, RevOps, Finance, GTM Engineering, and Security to ensure data reliability and correctness. The position emphasizes a hands-on approach, problem-solving, automation, and collaboration within a fast-paced, agent-first operating environment.
Responsibilities
- Own the reliability, freshness, and scaling of ingestion and orchestration pipelines into Snowflake, including monitoring and alerting.
- Design, build, and scale core dbt models for various reporting needs.
- Partner with business teams to define metrics, codify them in the warehouse, and resolve data quality issues.
- Manage Snowflake RBAC, dynamic masking, and PII classification for secure data access.
- Own the reverse-ETL layer to deliver warehouse data to downstream systems and agents.
- Extend semantic views and context for AI agents to accurately answer business questions.
- Build and advance CI/CD review gates for the data-platform monorepo.
- Own the infrastructure for the data platform, including compute, deployments, and access patterns.
Requirements
- Experience owning the reliability, freshness, and scaling of data pipelines.
- Experience designing, building, and scaling dbt models.
- Experience partnering with business stakeholders to define metrics and resolve data issues.
- Experience managing data warehouse access controls and data security policies.
- Experience building reverse-ETL processes.
- Experience extending data models and semantic views for AI agent consumption.
- Experience with CI/CD practices in a data platform context.
- Experience owning data platform infrastructure, including compute and deployments.
- Ability to identify data discrepancies, trace them through the data pipeline, and implement fixes.
- Proactive problem-solving and automation mindset.
- Enjoy collaboration with different business parts and finding scalable solutions.