Senior Data Engineer

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. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on. Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers.

We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale.

We practice software engineering for data. Pipelines are code with tests, CI/CD, and infrastructure-as-code behind them. Schema changes flow through automated migration tooling. Data quality is an engineered property of the system, not a dashboard someone checks. As a Senior Data Engineer, you will own ingestion pipelines end to end, from source system to warehouse to modeled, well-documented data products, across our stack of Snowflake, dbt, Dagster, Terraform, and AWS.

You will also work in one of the most AI-forward engineering environments anywhere. We operate production autonomous agents that triage incidents, review data, and ship code alongside us. You'll delegate real work to agents, review their output critically, and extend the shared knowledge and tooling they run on. We're looking for a strong software engineer who chose to specialize in data and is genuinely energized by working this way.

Responsibilities:

- 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.

- 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.

Requirements:

- 5+ 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).

- Hands-on depth in a modern MPP/OLAP warehouse or query engine, including the internals: query profiling, clustering and partitioning, cost attribution, and performance tuning. Snowflake preferred; experience with Trino, ClickHouse, Spark, BigQuery, or similar also counts.

- Experience building and operating orchestrated pipelines (Dagster, Airflow, or similar) and analytics engineering with dbt or equivalent.

- A track record of owning systems in production: monitoring, incident response, and the fo …