Senior Data Engineer

Runpod is the AI Developer Cloud. More than million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on 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 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 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 follow-through to leave things better than you found them.

• Demonstrated ability to use AI tools to improve the speed and quality of your work, with critical evaluation and verification of AI-assisted output.

• Excellent written communication. On a small distributed team working with agents, clear documentation is how decisions propagate.

• Nice to Have:

• Infrastructure-as-code experience (Terraform preferred) and comfort operating in AWS.

• Experience with streaming or near-real-time ingestion (Kinesis, Kafka, Snowpipe Streaming, or similar).

• Experience building with LLMs or agent frameworks (tool use, orchestration, evaluation).

• Startup experience: comfort with ambiguity, breadth, and shipping the MVP.

• What You’ll Receive:

• The competitive base pay for this position ranges from 175,000- 220,000 usd. This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location

• Meaningful equity in a fast-growing company- everyone on the team receives stock options — your impact drives our growth, and you share in the upside.

• Flexible PTO- take the time you need to recharge

• Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication

• Join a passionate team on the cutting edge of AI infrastructure — where culture, learning, and ownership are at the heart of how we scale.

• $1,200 Home Office & Equipment Stipend-

We set you up for success from day with gear and support to create your ideal workspace

Runpod is committed to maintaining a workplace free from discrimination and upholding the principles of equality and respect for all individuals. We believe that diversity in all its forms enhances our team. As an equal opportunity employer, Runpod is committed to creating an inclusive workforce at every level. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, protected veteran status, disability status, or any other characteristic protected by law. We welcome every qualified candidate eligible to work in the United States; however, we are currently unable to sponsor employment visas

• 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 follow-through to leave things better than you found them.
• Demonstrated ability to use AI tools to improve the speed and quality of your work, with critical evaluation and verification of AI-assisted output.
• Excellent written communication. On a small distributed team working with agents, clear documentation is how decisions propagate.
• Infrastructure-as-code experience (Terraform preferred) and comfort operating in AWS.
• Experience with streaming or near-real-time ingestion (Kinesis, Kafka, Snowpipe Streaming, or similar).
• Experience building with LLMs or agent frameworks (tool use, orchestration, evaluation).
• Startup experience: comfort with ambiguity, breadth, and shipping the MVP.