In a recent project I was optimizing query performance for a large sales database.

I needed to decide whether to add a clustered or non‑clustered index to improve read speed without impacting write performance.

I evaluated the table’s primary key usage, data modification patterns, and query plans. For tables with frequent range scans on a column that isn’t the primary key, I recommended a non‑clustered index. For tables where the primary key is the most accessed column and data is mostly read‑only, I suggested a clustered index to store rows physically in order.

The chosen indexing strategy reduced query response times by 45% and lowered I/O contention during peak hours.

Our production PostgreSQL server experienced a hardware failure that corrupted the last few transaction logs.

I was tasked with restoring the database to the exact moment before the corruption occurred, minimizing data loss.

I restored the latest full backup, applied incremental backups, and then used the WAL (Write‑Ahead Log) files to roll forward to the precise timestamp just before the failure. I verified consistency with checksum tools and performed a test restore on a staging environment first.

The database was recovered to within 2 seconds of the failure point, with zero data loss, and the service was back online within 45 minutes.

During a migration project, the legacy system used MySQL 5.5, which lacked native JSON support needed for a new feature.

I needed to persuade the dev team to switch to PostgreSQL 12, which offered robust JSON handling and better performance.

I prepared a proof‑of‑concept showing query speed improvements, documented migration steps, and highlighted security benefits like row‑level security. I held a joint workshop, addressed concerns about learning curve, and offered to create migration scripts and training sessions.

The team agreed to the switch, and after migration, the new feature’s response time improved by 60%, and we reduced security audit findings by 30%.

During a Black Friday sale, our primary Oracle database experienced a sudden outage due to a corrupted redo log.

My responsibility was to restore service within minutes to avoid massive revenue loss.

I immediately activated the standby replica, coordinated with the network team to reroute traffic, and initiated a point‑in‑time recovery using the most recent archived redo logs. Simultaneously, I communicated status updates to senior management and the e‑commerce team.

Service was restored in 7 minutes, downtime was limited to under 0.5% of peak traffic, and we avoided an estimated $250,000 loss. Post‑mortem led to implementing automated redo‑log monitoring.

The architecture team is designing a real‑time recommendation engine that must handle millions of requests per second.

Select a data store that meets latency, scalability, and availability requirements while fitting into the existing cloud ecosystem.

I evaluated options: distributed NoSQL (Cassandra), in‑memory cache (Redis Cluster), and NewSQL (CockroachDB). Considering the need for sub‑millisecond reads, strong consistency, and automatic sharding, I recommended a Redis Cluster with persistence enabled, complemented by a write‑behind pipeline to a durable PostgreSQL store for analytics. I outlined deployment via Kubernetes operators, automated scaling policies, and health checks.

The chosen solution delivered <1 ms read latency in load tests, scaled linearly with added nodes, and maintained 99.999% uptime during pilot, meeting the product’s SLA.

The DBA team spends ~30% of weekly time on manual index rebuilds, backup verification, and security patching across heterogeneous DB platforms.

Design an automation framework to handle these tasks reliably and securely.

I proposed using Ansible playbooks for cross‑platform task orchestration, combined with native DB tools (SQL Agent for SQL Server, pgAgent for PostgreSQL). For monitoring, I integrated Prometheus exporters and Grafana dashboards. All scripts are version‑controlled in Git, and CI/CD pipelines run nightly linting and unit tests. Security patches are applied via a staged rollout with automated compliance reporting to the audit team.

After implementation, routine maintenance time dropped by 70%, error‑related incidents fell by 40%, and audit compliance scores improved by 15%.