Staff Data Engineer: Lead Data Platform & AI Telemetry

Cribl; Salem (OR); Remote; USD 170,000 - 220,000; Full time

Benefits offered by this job

Health, dental, vision, short-term &{

Job summary

Cribl is seeking a Staff Data Engineer to lead and shape the data engineering platform. You will own architecture for core data systems, guide analytics and data science initiatives, and collaborate with IT, security, and business teams to deliver reliable, scalable data pipelines.

You will mentor junior engineers, advance dbt and the modern data stack, and contribute to secure, compliant engineering practices while supporting AI/LLM workflows in production.

Qualifications

• Deep expertise in Snowflake with performance tuning, cost management, and RBAC/security.
• Experience as a tech lead or staff-level engineer setting direction across teams.
• Strong Python and SQL fundamentals and ELT/data modeling experience.
• Hands-on experience with self-hosted orchestration (Prefect preferred; Airflow/Dagster acceptable).
• Hands-on AWS and infrastructure-as-code (Terraform) experience and cloud APIs.

Responsibilities

• Serve as a technical lead for the Data Engineering team.
• Own architecture and design decisions for core data systems and drive reliable, production-grade outcomes.
• Partner with stakeholders across functions (Data Analysts, SREs, IT, and business teams).
• Clarify requirements, validate outputs, and communicate risks and timelines.

Skills

Python

SQL

Data modeling

Leadership

Mentoring

Communication

Tools

Snowflake

Prefect

Airflow

Dagster

Terraform

CI/CD

Job description

Cribl is seeking a Staff Data Engineer to lead and shape the data engineering platform. You will own architecture for core data systems, guide analytics and data science initiatives, and collaborate with IT, security, and business teams to deliver reliable, scalable data pipelines.

You will mentor junior engineers, advance dbt and the modern data stack, and contribute to secure, compliant engineering practices while supporting AI/LLM workflows in production.