Staff Data Engineer
New
C
CriblIT and Security
Remote - United States, work happens across many time-zonesFull-TimeStaff
Salary$170,000 — $220,000 USD
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Job Details
- Required Skills
- AWSPythonSQLSnowflakeCI/CDTerraformData modelingdbt
Requirements
- Deep expertise in Snowflake (data modeling, performance tuning, cost management, security/RBAC).
- Demonstrated technical leadership as a tech lead or staff-level engineer.
- Experience mentoring and growing junior and mid-level engineers.
- Strong Python and SQL fundamentals with solid ELT patterns.
- Hands-on experience operating self-hosted/self-managed orchestration platforms (Prefect strongly preferred).
- Hands-on AWS and infrastructure-as-code experience (Terraform or equivalent).
- Experience building cloud applications or backend services (APIs, ingestion, event-driven workflows).
- Proficiency in production-grade engineering practices: logging, alerting, versioning, and CI/CD.
- Fluency building with AI-assisted development platforms (e.g., Claude Code).
- Excellent communication and cross-functional collaboration skills.
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 Engineers, and business teams).
- Own and operate self-hosted orchestration platform (e.g., Prefect) and manage its infrastructure.
- Manage the Snowflake environment including data modeling, performance, cost optimization, and governance.
- Build, operate, and monitor the core data tech stack, including pipelines, ingestion services, and integrations.
- Advance modern data stack practices, including dbt, and drive warehouse cost/performance at scale.
- Develop cloud-native services and infrastructure on AWS using infrastructure-as-code (Terraform).
- Mentor and grow junior and mid-level data engineers through code review and pairing.
- Support data science and agentic initiatives by preparing model-ready datasets and integrating AI/LLM workflows.
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