Staff Data Engineer

• APIs, ingestion services, and event-driven workflows.
• Production-grade engineering practices: logging, alerting, versioning, CI/CD.
• Fluency building with AI-assisted development platforms such as Claude Code, with a demonstrated ability to use them to accelerate delivery.

#LI-JB1

#LI-Remote

• This covers data pipelines, ingestion services, integrations, and the warehouse, ensuring data is accurate, timely, and trusted, with logging, alerting, and observability as first-class concerns.
• Advance the modern data stack. Drive our dbt practice forward, including the in-flight dbt Fusion migration, and own warehouse cost/performance at scale.
• Develop cloud-native services and infrastructure on AWS. This includes our ingest/API services (event-driven ingestion, upsert patterns, package reuse) and the IaC that supports them.
• Advance our infrastructure-as-code practice (Terraform or similar). Build reusable modules, clean deployment patterns, CI integration, and strong operational hygiene, including self-hosted/hybrid deployment patterns.
• Partner with the Data Engineering manager to set technical direction. Shape the platform roadmap, engineering standards, and build-vs-buy/architectural tradeoffs.
• Mentor and grow data engineers through code review, pairing, design feedback, and coaching that levels up junior and mid-level engineers.
• Support Cribl's growing data science and agentic initiatives. Prepare model-ready datasets, expose features, and integrate AI/LLM workflows into production.
• 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
• advanced data modeling, query and warehouse performance tuning, cost management, and security/RBAC design. This is the most important qualification for the role.
• Demonstrated technical leadership: experience as a tech lead or staff-level engineer setting direction, driving architecture, and delivering complex systems across a team.
• Excellent communication and cross-functional collaboration: able to work across data, engineering, and business teams and lead through influence.
• A track record of mentoring engineers: growing junior and mid-level engineers through coaching, review, and pairing.
• Strong Python and SQL fundamentals, with solid ELT patterns and data modeling experience.
• Hands-on experience operating self-hosted / self-managed orchestration. Prefect strongly preferred; equivalent experience running (not just authoring on) Airflow/Dagster on your own infrastructure is acceptable. Must be comfortable owning upgrades, workers, and the operational footprint, not only writing DAGs/flows.
• Hands-on AWS and infrastructure-as-code experience: building cloud infrastructure with Terraform (or equivalent) using predictable, maintainable patterns.