Key skills
SQLPythonSnowflakeAWSTerraformdbtData modelingCommunicationCollaborationAILLMELTAnalyticsRedshiftDatabricksAirflowCI/CD
About this role
Cribl is a remote-first company focused on empowering employees to excel in their roles. They are seeking a Data Engineer to build and scale systems that support analytics and operational decision-making, contributing to a collaborative and innovative culture.
Responsibilities:
- Build, operate, and monitor Cribl’s core data tech stack including data pipelines, data integrations and our data warehouse ensuring data is accurate, timely, and trusted
- Develop cloud-native services and infrastructure that power scalable, reliable data systems, with logging, alerting, and observability as first-class concerns
- Contribute to infrastructure-as-code (Terraform or similar), clean deployment patterns, and operational hygiene
- Support Cribl’s growing data science and agentic initiatives by preparing model-ready datasets, exposing features, and integrating AI/LLM workflows into production systems
- Work closely with Analysts and business stakeholders to clarify requirements, validate data outputs, and translate business logic into reliable data artifacts
- Partner closely with Data Analysts, Site Reliability Engineers, and IT Engineers on initiatives that align with business needs to clarify requirements and validate data outputs
- Communicate risks, tradeoffs, and timelines proactively to keep work predictable
- Contribute to secure, compliance-minded engineering practices in collaboration with IT/Security
- We are a remote-first company and work happens across many time-zones - you may be required to occasionally perform duties outside your standard working hours
Requirements:
- Strong SQL and Python fundamentals; experience with ELT patterns and data modeling
- Exposure to Snowflake or a similar cloud warehouse (databricks, redshift, duckdb), and familiarity with dbt or equivalent frameworks
- Experience building cloud applications or backend services (APIs, ingestion services, event-driven workflows)
- Hands-on experience with AWS cloud infrastructure and infrastructure-as-code such as Terraform
- Familiarity with workflow orchestration (e.g., Prefect, Airflow) and production-grade engineering practices (logging, alerting, versioning, CI/CD)
- Clear, concise communication and the ability to collaborate across data, engineering, and business teams