TensorStax is building the next generation of autonomous agents for data engineering. Backed by a $5M seed round. The Role As Data Engineer you will design, build, and optimize production-grade pipelines that our agents learn from and eventually operate. You will own the modeling layer in dbt, the orchestration layer in Airflow, and the heavy-lift workloads in Spark. What You’ll Do Model complex, interdependent schemas in dbt across hundreds of tables Build advanced, multi-branch Airflow DAGs with sophisticated dependency and failure handling Author high-performance Spark jobs (PySpark or Scala) for large-scale batch and incremental workloads Codify lineage, testing, and metadata so agents can reason about pipeline state Profile and tune query performance across warehouses and lakehouse engines Partner with the agent research team to expose realistic failure modes, data drifts, and SLA violations for RL training Containerize and deploy everything on Kubernetes-backed infra About You 4+ years in data engineering or analytics engineering, shipping pipelines at scale Deep experience with dbt, including macros, custom tests, and refactoring legacy models Track record building and debugging complex Airflow DAGs (Sensors, TaskGroups, SubDAG patterns) Spark power-user capable of distributed joins, window functions, and memory tuning Solid Python, Git, and CI discipline Bonus: experience with Iceberg, Delta, or DataFusion; prior RL or agent work Why TensorStax Write the pipelines our autonomous agents learn to operate Work in a tight, senior team that values clean code and measurable impact Competitive salary, meaningful equity, and hardware budget Remote-first with optional SF office