Senior Customer Success Engineer

Stack

Responsibilities

• ▹Partner closely with customers to design, deploy and optimize LanceDB in production environments
• ▹Lead technical onboarding and architecture reviews, providing guidance on configuration, query optimization and integration patterns
• ▹Proactively identify adoption barriers, troubleshoot complex distributed-system issues and coordinate resolutions with product and engineering teams
• ▹Own customer success metrics such as deployment time, usage growth, retention and satisfaction; build dashboards and track account health
• ▹Develop and deliver technical enablement, including sample code, automation tools and documentation
• ▹Serve as the customer's technical advocate internally, communicating feature requests and influencing roadmap priorities
• ▹Collaborate cross-functionally with sales engineering and support engineering on evaluations, proofs-of-concept, demos and escalations
• ▹Contribute to internal tooling, runbooks and playbooks for the customer success organization

Requirements

• ▹10+ years of professional experience in technical roles such as post-sales engineering, customer success, solutions architecture or technical support, ideally within the data infrastructure or distributed systems space
• ▹Proven track record supporting or deploying distributed database systems or large-scale cloud-native data platforms
• ▹Strong proficiency in Rust and Python, able to read, debug and write production-grade code in both
• ▹Deep understanding of distributed systems concepts, including sharding, replication, consensus, partitioning, failure recovery and performance tuning
• ▹Experience deploying and managing workloads on Kubernetes or other container orchestration frameworks, and familiarity with cloud environments such as AWS, GCP and Azure
• ▹Exceptional communication skills

Soft skills

About the company

LanceDB is a high-performance, open-source, cloud-native data platform built for AI-native and multimodal workflows, powering vector search at multi-billion scale, real-time retrieval, feature engineering and analytics.