11 days ago
Remote, WorldwideStaff+
Responsibilities
- Own and evolve the architecture of data extraction, loading, and pipeline systems supporting more than 500 integrations.
- Identify architectural bottlenecks and drive performance and reliability improvements across distributed systems.
- Lead modernization of legacy systems, including LES, DSAS, and ULS, into scalable infrastructure.
- Design and implement agentic development frameworks for codebase maintenance, continuous improvement, automated acceptance, and rollout.
- Define and document technical decisions and establish standards for code quality, reliability, performance, validation, and testing.
- Mentor engineers and promote responsible AI-assisted development practices.
- Collaborate with product and AI teams on agentic framework rollout and new integration capabilities.
Requirements
- 7+ years of hands-on backend engineering experience, including at least 2 years in a principal- or architect-level role owning production systems.
- Experience designing and scaling complex systems.
- Hands-on experience with AI-assisted development, Claude Code, agentic coding, and building agentic frameworks for code maintenance and rollout.
- Strong distributed systems experience with Kubernetes, workflow orchestration such as Temporal, and message queues such as RabbitMQ.
- Experience building secure, resilient systems.
- Experience with observability, alerting, and incident response at scale.
- Solid SQL and query optimization experience across PostgreSQL and ClickHouse.
- Ability to work within the EST timezone and fluency in English.
- Preferred qualifications include defining and driving adoption of engineering standards, migrating legacy pipeline systems, and experience with data integration or ETL infrastructure.
Benefits
- Remote-first work environment.
- 20 PTO days and US holidays.
- Stock options.
- Professional development reimbursement.
- Optional relocation assistance to Latin America after the trial period.
- Direct impact on platform architecture and freedom to experiment with an AI-native technology stack.
Tech Stack
Categories
BackendData Engineering