Staff-Level Database Administrator Interview Preparation Guide (FAANG Standards)

Your reasons for interest in this specific company and role, how your values align with company culture, and what excites you about the opportunity. Demonstrate you've researched the company.

Clear articulation of your professional journey in database administration, progression from junior to staff level, key roles, and major accomplishments. Include experience with different database systems, team sizes managed, and scale of systems you've worked with.

Examples of how you've influenced team members, mentored junior DBAs, contributed to architectural decisions, and influenced technical direction. Highlight situations where your expertise shaped database strategy.

Recognition and avoidance of common query patterns that cause performance issues: cartesian joins, N+1 queries, inefficient subqueries, functions in WHERE clauses, SELECT *, and other pitfalls that degrade performance.

Understanding and interpreting query execution plans, identifying performance bottlenecks, using EXPLAIN/EXPLAIN PLAN output, understanding index usage, join algorithms, and optimization techniques. Knowledge of how to read cost estimates and recognize inefficient query patterns.

Understanding different index types (B-tree, hash, bitmap), when to use indexes, composite indexes, covering indexes, index maintenance overhead, and trade-offs between query speed and write performance. How indexing impacts both performance and storage.

Complex SQL query construction including multi-table joins, subqueries, common table expressions (CTEs), window functions, aggregations, and set operations. Writing queries that are both correct and efficient for large datasets.

Understanding different constraint types (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, NOT NULL), enforcing referential integrity, cascading deletes/updates, handling constraint violations, and how constraints protect data quality.

Backup strategies (full, incremental, differential), Recovery Point Objective (RPO) and Recovery Time Objective (RTO), restore procedures, point-in-time recovery, backup validation, disaster recovery planning, and high-availability architecture. Understanding WAL (Write-Ahead Logging) and its role in durability.

Database schema design principles, normalization forms (1NF through BCNF), denormalization trade-offs, primary and foreign keys, referential integrity, and designing schemas for both performance and maintainability. Understanding when to normalize vs denormalize.

Deep understanding of Atomicity, Consistency, Isolation, and Durability properties. Transaction isolation levels (READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE), dirty reads, phantom reads, non-repeatable reads, and trade-offs between consistency and performance.

User authentication and authorization, role-based access control (RBAC), principle of least privilege, encrypted connections, data masking, audit logging, and compliance requirements (HIPAA, GDPR, SOC 2).

When to use different database types (relational, NoSQL document stores, key-value stores, time-series databases, search engines), technology evaluation criteria, data consistency across heterogeneous systems, and managing complexity of multiple databases.

Growth projections, capacity requirements calculation, headroom planning, storage scaling, query performance at scale, and architectural changes needed as data grows. Understanding vertical vs horizontal scaling implications.

Master-slave replication, multi-master replication, synchronous vs asynchronous replication, handling replication lag, automatic failover, split-brain scenarios, and consistency guarantees in replicated systems. Understanding RPO/RTO implications of different architectures.

Horizontal scaling through data sharding, sharding key selection, consistent hashing, handling hot shards, resharding during growth, and trade-offs between sharding strategies. Understanding range-based, hash-based, and directory-based sharding.

Understanding the CAP theorem (Consistency, Availability, Partition tolerance), making architectural trade-offs based on business requirements, choosing appropriate consistency models (strong consistency, eventual consistency), and understanding implications of partition scenarios.

Memory management (buffer pools, cache), CPU utilization, disk I/O patterns (sequential vs random access), SSD vs HDD considerations, storage tier strategy, and resource contention resolution.

Systematic approach to identifying performance bottlenecks, analyzing query performance, optimizing indexes, refactoring problematic queries, improving cache utilization, and measuring impact of optimizations. Understanding performance baselines and setting realistic targets.

Understanding locking mechanisms, deadlock detection and prevention, lock escalation, transaction isolation levels and their performance implications, optimistic vs pessimistic locking strategies, and handling high-concurrency scenarios.

Systematic approach to diagnosing failures: data collection, root cause analysis, isolating problems to specific components, reproducing issues, implementing fixes without downtime when possible, and post-incident analysis to prevent recurrence.

Key database metrics (query latency, throughput, CPU, memory, disk I/O, cache hit ratios, lock contention), designing effective monitoring strategies, setting meaningful thresholds and alerts, time-series data collection, and dashboards for system health. Preventing alert fatigue.

Explaining complex technical concepts to non-technical audiences, presenting to leadership, writing documentation, and persuading teams to adopt improvements. Examples of successfully communicating technical trade-offs.

Working effectively with software engineers, DevOps teams, product managers, and executives. Examples of complex situations requiring coordination, managing competing priorities, and building consensus.

Examples of significant failures or setbacks, how you responded, what you learned, and how you prevented similar issues. Demonstrating accountability and growth mindset.

Examples of mentoring junior and mid-level DBAs, developing people, creating learning opportunities, influencing technical decisions, and driving team improvement. Demonstrating how you've grown talent and built capability.

How you evaluate technology choices, make architectural trade-off decisions, involve stakeholders, balance short-term and long-term concerns, and influence teams toward the chosen direction. Examples of major technical decisions and their outcomes.

How you evaluate new database technologies, making technology adoption decisions, balancing innovation with stability, prototyping new approaches, and building organizational capability with emerging technologies.

How your database decisions impact product delivery, developer productivity, and business outcomes. Examples of how you've enabled new capabilities, unblocked teams, or prevented business-critical issues.

Designing systems that survive catastrophic failures, designing geo-redundancy, handling multi-region failover, recovery from complete data center failures, and testing disaster recovery procedures. Understanding chaos engineering principles.

Designing database systems handling petabyte-scale data, designing for global distribution across data centers, handling eventual consistency, managing data sovereignty and compliance requirements, and architectural decisions in complex multi-region setups.