Senior Software Engineer II, Managed Databases

About the role

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. We are looking for a Senior Software Engineer II to join our Managed Databases (DBaaS) engineering organization. Managed Databases delivers fully managed Postgres, MySQL, and related database services so customers can run production data workloads—and increasingly AI inference and data-intensive applications—without operating clusters themselves. In this high-impact IC4 role, you will apply advanced subject-matter expertise across distributed control planes, database platform infrastructure, and production reliability. You will bridge product and system requirements with concrete architecture, lead complex technical projects across DBaaS and adjacent platform boundaries (e.g., Networking, Droplets, Storage, engine teams), and mentor engineers to elevate standards on the team. You should be comfortable independently moving from ambiguous problems to design, implementation, launch, and long-term operation.

Responsibilities

• Architect and implement DBaaS platform systems: Design and build scalable control-plane and infrastructure components that improve throughput, multi-region operability, and reliability for managed database workloads.
• Turn ambiguous requirements into deliverable architecture: Deconstruct customer and system needs into clear technical designs and workstreams that drive predictable delivery with product and engineering partners.
• Own performance and resiliency outcomes on your services: Improve resource utilization, fault tolerance, and availability—using profiling and targeted changes against I/O, CPU, and network bottlenecks under real production load.
• Strengthen lifecycle capabilities: Ship improvements to automated failover, continuous backups, point-in-time recovery (PITR), and maintenance workflows so clusters handle higher data volumes and tighter SLAs.
• Scale for data-heavy and AI-adjacent demand: Expand and harden control-plane and infrastructure pieces that support compute-heavy and high-concurrency customer workloads.
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