You're an experienced database technologist. You have at least 7 years in systems administration, systems engineering, or software development, with at least 4 years running production relational databases. You have deep expertise in MySQL or PostgreSQL and working knowledge of the other, including each engine's ecosystem of extensions and tools. You understand caching (such as Redis) as part of the data layer, how to design and operate it at scale, and the tradeoffs between caching and the database underneath.
You know the cloud and treat infrastructure as code. You've designed and maintained highly available, cloud-based database systems in AWS or another cloud platform, and you manage that infrastructure with Terraform or similar IaC tooling rather than by hand. You're comfortable operating stateful services alongside Kubernetes or other distributed systems, and you understand the tradeoffs of running data workloads in and around orchestrated environments.
You build and use observability. You've implemented metrics, logging, and alerting for data systems using tools like Datadog, Prometheus, or CloudWatch, and you know the difference between a dashboard that looks good and one that catches problems before customers do. You treat query performance data, replication lag, and cache hit rates as first-class signals.
You partner with product teams. Product teams are your customers. When a team comes to you with a design or a problem, you ask about their workload before prescribing a solution, give them options with tradeoffs rather than edicts, and leave them more capable than you found them. You handle support requests with the same care as incident response, you participate in an on-call rotation for production data systems, and you'd rather teach a team to fish than field the same question twice. You document things well so your knowledge can be consumed asynchronously.
You raise the bar for the team around you. You're an experienced DBRE on this team, and you treat that as a responsibility. You review peers' designs and give feedback that makes the design better and the engineer sharper. You pair on hard problems when it helps someone else grow. You turn things you've learned the hard way into docs, patterns, and runbooks so the next person doesn't have to learn them the hard way too. You care about the team being more capable a year from now than it is today.
You ship code to solve systems problems. You automate repetitive work and build tools that make the team more effective. You're comfortable digging into application code to fix a bug or launching a backend service to close a gap in our data management strategy. You take ownership of difficult problems and know that "just throw more hardware at it" isn't a scaling strategy.
You work AI-first. AI tools are already part of how you operate: you use them to write and review automation code, analyze query plans and logs, draft runbooks, and accelerate incident investigation. You have real judgment about where AI output is reliable and where it isn't, especially against production data systems, and you're building that fluency into your team's practices, not just your own.