$200,000–$230,000 Per Year
Amazon Relational Database Service (RDS), Artificial Intelligence (AI), Automation, Autoscaling, Best Practices, Caching, Communication Skills, Continuous Improvement, Data Partitioning, Data Quality, Database Technology, Distributed Computing, Distributed Databases, Engineering, High Availability, Large-Scale Systems, Machine Tool, Mentoring, Model View Controller, MySQL, On Call, Operational Improvement, Operations Planning, Performance Analysis, Performance Reviews, PostgreSQL, Process Improvement, Product Development, Product Engineering, Production Systems, Query Optimization, Redis, Ruby on Rails, Software Engineering, Standards Development, System Lifecycle, Transaction Processing/Management
About the Role:
Were looking for an experienced engineer with deep expertise in distributed data systems to shape the future of Gustos storage layer. Youll manage complex migrations, architect high-scale systems, and set standards for automation, resiliency, and security. This is a high-impact role where youll implement distributed database solutions that enable Gustos continued growth and scale.
About the Team:
The Datastores Infrastructure Engineering team designs, builds, and operates the data platforms that power Gustos product: MySQL, Postgres, Redis, Kafka, and S3. We ensure our infrastructure is consistent, reliable, and ready to support Gustos growing needs. As we transition to self-hosted distributed databases, the team is focused on reducing blast radius, improving operational resilience, and enabling sustainable scale.
Day-to-Day Responsibilities:
- Architect, deploy, and own the full lifecycle of distributed database systems (TiDB) on Kubernetes at scale, ensuring high availability, data consistency, and operational excellence
- Coordinate complex, zero-downtime migrations from monolithic to distributed architectures, including vertical sharding to isolate Product Services
- Define and drive efficiency improvements across the storage infrastructure through query optimization, caching strategies, and workload management
- Define standards and build reliable automation to ensure data consistency, integrity, and security across distributed systems
- Continuously improve operational excellence by reducing on-call burden through sustainable, long-term solutions
- Partner with product engineering teams and technical collaborators to enable rapid and reliable product development
- Mentor engineers across the Datastores Infrastructure team on best practices for operating complex, self-hosted distributed systems, actively developing our collective operational expertise
Requirements:
- 12+ years of software engineering experience building and scaling large-scale infrastructure systems
- Hands-on experience building and operating distributed databases on Kubernetes (strongly preferred: TiDB; alternatively: CockroachDB, Vitess, Citus, or similar solutions)
- Deep expertise in distributed data systems including horizontal sharding, partitioning strategies, and distributed transaction management
- Proven experience coordinating complex, zero-downtime migrations affecting production systems at scale
- 5+ years of AWS experience with RDS, Aurora, caching systems (Redis/ValKey), streaming platforms (Kafka), and infrastructure optimization at scale
- Strong communication skills with the ability to simplify technical complexity and collaborate on technical direction across teams
- Curiosity and ability to operate in an AI-native environment, leveraging AI tooling to enhance infrastructure operations, driving improvements in query optimization, performance evaluation, and infrastructure automation
Bonus Requirements:
- Experience with service extraction and vertical sharding from monolithic architectures
- Experience working with Ruby on Rails or similar MVC frameworks at scale
Compensation:
Our cash compensation amount for this role is targeted at $200,000-$230,000 /yr in Denver & most remote locations, and $230,000-$270,000 /yr for San Francisco, Seattle & New York. Final offer amounts are determined by multiple factors, including candidate experience and expertise, and may vary from the amounts listed above.