Senior Data Platform Engineer — Build Scalable AI 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

Job summary

CoreWeave is seeking a Senior Data Platform Engineer to design and operate an enterprise data platform, with a focus on scalable data pipelines, orchestration, and telemetry. You will partner with software and analytics teams to enable data-informed AI applications and improve data models for large-scale workloads.

You will apply distributed-systems knowledge, design patterns, and production data practices to ensure reliability, performance, and cost efficiency across multi-petabyte

Qualifications

• Bachelor’s degree or foreign equivalent in a technical field.
• At least five years of professional software or data engineering experience.
• Experience designing production data pipelines and distributed processing.
• Experience with dbt, Dagster, or Airflow.
• Strong SQL skills and data warehouse concepts.

Responsibilities

• Design and operate scalable data platforms and pipelines across production systems.
• Develop data transformation and orchestration frameworks with reliability and observability.
• Improve data warehouse performance, modeling, and cost efficiency.
• Collaborate with software teams on telemetry and instrumentation.
• Define metadata, governance, and semantic models for analytics.
• Support data-powered AI applications and on-call platform support.

Skills

Python

SQL

Distributed systems

Data modeling

ETL pipelines

Cloud/AWS

Communication

Education

Bachelor’s degree in CS/Engineering

Tools

dbt

Dagster

Airflow

Job description

CoreWeave is seeking a Senior Data Platform Engineer to design and operate an enterprise data platform, with a focus on scalable data pipelines, orchestration, and telemetry. You will partner with software and analytics teams to enable data-informed AI applications and improve data models for large-scale workloads.

You will apply distributed-systems knowledge, design patterns, and production data practices to ensure reliability, performance, and cost efficiency across multi-petabyte