Senior Data Platform Engineer — Scale AI Data Pipelines

Weights & Biases; San Francisco (CA); On-site; USD 165,000 - 242,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with employer match

Paid Parental Leave

Flexible PTO

Tuition Reimbursement

Employee Stock Purchase Program (ESPP)

Mental Wellness Benefits

Job summary

CoreWeave is seeking a Senior Data Platform Engineer to partner with Data Platform Engineers and Data Scientists to expand and maintain our data platform. You will work with core engineering to improve product telemetry and eventually join the on-call rotation for data infrastructure.

Key objectives include migrating data build tools, building an internal data catalog and semantic layer for self-service analytics, and laying foundations for internal AI applications built on top of operational

Qualifications

• Deep knowledge of data engineering workflows.
• Ability to write clear, maintainable code and documentation for projects.
• Experience with large data warehouses and high-demand systems.
• Familiarity with web apps logging and instrumentation for telemetry.

Responsibilities

• Maintain and expand the data platform in collaboration with Data Platform Engineers and Data Scientists.
• Improve product telemetry with core engineering.
• Join on-call rotation for the data platform.
• Migrate data build tools to improve efficiency and performance; build a data catalog and semantic layer; build an internal AI agent; lay foundations for AI applications built on operational data.

Skills

Data engineering

Documentation

Clear communication

Data platform knowledge

Tools

dbt

dagster

airflow

Job description

CoreWeave is seeking a Senior Data Platform Engineer to partner with Data Platform Engineers and Data Scientists to expand and maintain our data platform. You will work with core engineering to improve product telemetry and eventually join the on-call rotation for data infrastructure.

Key objectives include migrating data build tools, building an internal data catalog and semantic layer for self-service analytics, and laying foundations for internal AI applications built on top of operational