Thanks for taking the time, Alex! Let's start with your recent backend work at your current startup. Walk me through the architecture of your distributed worker queue.

Sure. We were hitting throughput limits when traffic spiked to 12k requests per second. I re-architected our Redis queue into partition-aware Go worker pools with Postgres optimistic locking to handle concurrency safely.

How did you deal with database index bloat and query contention during high write spikes?

We isolated write-heavy telemetry from transactional rows into BRIN-indexed tables, which cut our index memory footprint by nearly 65%. I walked the team through the query optimization trade-offs before rolling out.

How do you communicate those architectural trade-offs to product managers who just want features shipped faster?

I translate database contention into user latency impact and AWS infrastructure cost savings. When they see latency drops from 400ms to 45ms, the business case is clear.

During our database failover incident, our team did a blameless post-mortem. I made sure to give credit to our junior on-call engineer who caught the replica lag early.