Senior Data Platform Engineer - Weights & Biases

Role overview Design and operate a large-scale data platform that supports analytics, product telemetry, and data-powered applications. This senior engineering role spans distributed pipelines, warehouse performance, orchestration, data governance, and production operations, with a focus on reliable systems at multi-petabyte scale.

Responsibilities - Architect, build, test, and maintain batch and streaming pipelines for structured and semi-structured data. - Develop transformation and orchestration workflows, setting standards for testing, deployment, dependencies, data quality, and observability. - Design analytical data models, schemas, partitioning, and storage strategies; improve warehouse performance, reliability, and cost efficiency. - Partner with software teams to define event schemas and implement telemetry and instrumentation across applications and services. - Build metadata, lineage, governance, and semantic-layer capabilities for consistent, secure self-service analytics. - Monitor production systems, troubleshoot incidents, document operating procedures, and join an on-call rotation after onboarding.

Requirements - Experience operating multi-petabyte platforms or other high-volume, business-critical data systems. - Strong software engineering, data architecture, distributed systems, and database knowledge. - Production programming experience in Python, Go, Java, Scala, or a comparable language, plus strong SQL skills. - Experience with warehouse data models and performance tuning, data-quality testing, and production monitoring. - Familiarity with engineering practices such as version control, automated testing, code review, continuous integration, and deployment automation. - Ability to communicate technical designs and tradeoffs across engineering, data science, analytics, and business teams.

Nice to have - Cloud infrastructure experience with AWS, Google Cloud, or Microsoft Azure. - Experience with orchestration tools such as dbt, Dagster, or Airflow; data catalogs, lineage, governance, or semantic layers. - Knowledge of web telemetry, APIs, distributed services, or data needs for machine-learning and production AI workflows. - Experience supporting platform users and turning feedback into improvements.

Benefits and work setup The stated US base salary range is $165,000–$242,000, with final compensation based on qualifications, experience, interview performance, and location. Eligible rewards include bonus and equity, health and insurance coverage, retirement matching, flexible paid time off, parental and family support, and tuition reimbursement. Benefits vary by location.