The opportunity
Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform.
What you'll do
Own the design, implementation, and operation of data pipelines end to end:: batch and near-real-time streaming ingestion into Snowflake, bronze-to-silver transforms in dbt, and the orchestration and infrastructure that support them.
Build well-documented, high-quality data products that are modeled, tested,: and easy to understand, and that analytics, finance, and engineering teams rely on for critical decisions.
Treat data quality and observability as part of every deliverable: dbt tests, freshness and anomaly monitoring, lineage, and alerting that stays trustworthy. A noisy alert is a defect.
Diagnose and optimize warehouse cost and performance across query profiles,: clustering, and warehouse sizing, and verify claims with measurement before shipping changes.
Debug production data incidents independently: root-cause across the pipeline, assess blast radius, fix, and verify downstream impact.
Manage infrastructure as code (Terraform for AWS and Snowflake) with the: discipline that entails. In our world, merge is deploy.
What they're looking for
- Work daily with AI agents: delegate well-scoped work, verify and own the correctness of AI-assisted output, and contribute skills and documentation that make the agents (and the humans) more effective.
- Partner cross-functionally to turn business questions into data requirements,: and data requirements into shipped, maintained systems.
- + years of professional software engineering experience, with at least 3: years focused on data engineering in modern cloud environments.
- Strong Python and advanced SQL, applied with an engineer's discipline: code that is tested, reviewed, and built to be maintained. Fluency in other languages is a plus (e.g. Go, Rust, Scala).
