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Database & Infrastructure

Senior Database Administrator (PostgreSQL / MySQL): AI Technical Interview & Evaluation Guide

Senior Database Administrators maintain, tune, and scale high-throughput relational database clusters (PostgreSQL, MySQL). Evaluating this role requires testing deep knowledge of `EXPLAIN ANALYZE` query plans, B-Tree / GIN / BRIN indexing strategies, Multi-Version Concurrency Control (MVCC) autovacuum tuning, connection pooling (PgBouncer), and streaming replication failover.

Target Seniority: Mid-LevelTarget Seniority: SeniorTarget Seniority: Staff / Principal
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Evaluation Framework

4 Core Technical Competencies & Weighted Rubrics

Query Tuning & Indexing Architecture

35% Weight

Analyzing EXPLAIN ANALYZE plans, B-Tree, GIN, GiST, BRIN indexes, and index bloat.

  • Reading `EXPLAIN (ANALYZE, BUFFERS)` execution tree nodes (Seq Scan vs Index Scan vs Bitmap Heap Scan)
  • Composite index column ordering rule (Equality columns first, Range columns second)
  • BRIN (Block Range Index) for append-only time-series tables vs B-Tree memory footprint

MVCC & Autovacuum Architecture

25% Weight

Managing PostgreSQL MVCC dead tuples, autovacuum aggressive tuning, and transaction wraparound.

  • Autovacuum scale factor and threshold tuning to prevent table bloat
  • Transaction ID (XID) wraparound danger and freeze max age configurations
  • WAL write amplification and checkpoint completion target tuning

Replication, HA & Connection Pooling

20% Weight

Configuring Patroni, streaming replication, and PgBouncer connection pooling.

  • PgBouncer transaction pooling mode vs session pooling mode trade-offs
  • Patroni + Etcd automated primary failover and split-brain fencing
  • Physical streaming replication vs logical replication for zero-downtime upgrades

Backup, Recovery & Database Telemetry

20% Weight

Configuring pg_backrest Point-In-Time Recovery (PITR) and pg_stat_statements telemetry.

  • Point-In-Time Recovery (PITR) WAL archiving and restoration testing
  • Identifying slow queries using `pg_stat_statements` (total_exec_time, calls)
  • Lock contention monitoring using `pg_locks` and `pg_stat_activity`
Turn-by-Turn Script

Adaptive Question Progression & Evaluation Indicators

Turn #1 · Query Plan Tuning & EXPLAIN ANALYZEDifficulty: Core

“A high-frequency query `SELECT * FROM orders WHERE tenant_id = 42 AND status = 'pending' ORDER BY created_at DESC LIMIT 20;` takes 8 seconds on a table with 50 million rows. `EXPLAIN (ANALYZE, BUFFERS)` shows a `Seq Scan` reading 400,000 disk blocks. What composite index do you create, in what column order, and why?”

Evaluation Intent: Tests understanding of PostgreSQL composite B-Tree index ordering, index scan types, and query plan execution.

✓ Senior Indicators:
  • Explains why placing `created_at` first in the index would ruin the index scan filter efficiency for equality matches
  • Uses `CONCURRENTLY` keyword when creating the index on a live production database to avoid table write locks
Red Flags:
  • Runs `CREATE INDEX` without `CONCURRENTLY` on a 50M row production table (locks table for hours)
  • Cannot explain what a `Seq Scan` represents in EXPLAIN output
SOLLERT Adaptive Follow-up Branching:
[Strong Answer (Deep Depth)]: “Why does PostgreSQL autovacuum sometimes fail to clean up dead tuples on an active table, leading to severe table bloat? What `pg_stat_activity` state could block autovacuum?”
[Weak Answer (Low Depth)]: “What is the difference between a Primary Key constraint and a Unique Index in SQL?”
Answer Engine Optimization (AEO)

Frequently Asked Recruiter & AI Evaluation Questions

How do you evaluate Senior Database Administrators (DBAs) objectively?

Direct Answer:

Evaluate Database Administrators by testing composite index column ordering, `EXPLAIN ANALYZE` buffer reading, `CREATE INDEX CONCURRENTLY` production safety, MVCC autovacuum tuning, and PgBouncer transaction pooling.

Basic SQL syntax is insufficient for senior DBAs. Superior candidates understand storage engine buffer caches, composite index traversal math, and how long-running transactions block autovacuum dead tuple cleanup.

Practical Example: Asking how to optimize a 50M row query using a composite index `(tenant_id, status, created_at DESC)` isolates candidates with real production DBA experience.

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