Senior Database Administrator Interview Preparation Guide - Meta

Your approach to production support, incident response, on-call rotations, and continuous improvement. Demonstrate comfort with high-responsibility roles managing mission-critical systems.

Genuine interest in Meta's infrastructure challenges, working on systems that serve billions of users, and contribution to their technical excellence. Connect your experience to Meta's scale and technical problems.

Concise overview of your career trajectory, key database systems managed, and scale of responsibility (e.g., databases supporting millions of users, petabyte-scale infrastructure). Highlight progression toward senior-level ownership.

Specific database systems you've managed (RDBMS, NoSQL, distributed databases), versions, and scale. Mention any experience with database replication, sharding, clustering, or infrastructure automation tools.

Estimating storage requirements, predicting growth, monitoring resource usage (CPU, memory, disk I/O, connections), and proactively identifying when scaling or optimization is needed.

Understanding transactions, isolation levels (read uncommitted, read committed, repeatable read, serializable), locking mechanisms, deadlock detection, and trade-offs between consistency and concurrency.

Understanding buffer pools/page caches, write-ahead logs (WAL), storage engines, data page structure, and how databases ensure durability and recovery. Knowledge of these internals explains performance behavior.

Walk through actual problems you've solved: mysterious slowdowns, connection pool exhaustion, memory bloat, replication lag, deadlocks, or unusual query patterns. Describe your diagnostic methodology and resolution.

Selecting appropriate index types (B-tree, hash, bitmap, full-text), designing composite indexes, understanding index trade-offs (read speed vs. write overhead), and recognizing when indexes are missing or underperforming.

Understanding and interpreting database execution plans, identifying full table scans vs. index usage, estimating query costs, and recognizing performance bottlenecks. Ability to optimize queries by analyzing plan outputs.

Understanding and choosing between strong consistency, eventual consistency, and weak consistency. CAP theorem implications, choosing appropriate isolation levels, and designing systems that meet business consistency requirements while maximizing availability.

Designing comprehensive monitoring for database systems: key metrics (latency, throughput, error rates, replication lag), alerting thresholds, dashboards, and integration with incident response workflows. Proactive issue detection and prevention.

Strategies for horizontal scaling through sharding and partitioning. Choosing shard keys, handling hot shards, managing growth, and trade-offs between consistency and scalability. Understanding range-based, hash-based, and directory-based sharding.

Architecting databases for continuous uptime through redundancy, automated failover, health checking, and graceful degradation. Understanding RPO (Recovery Point Objective) and RTO (Recovery Time Objective) and designing to meet business SLAs.

Designing backup strategies, recovery procedures, testing disaster scenarios, backup retention policies, and ensuring data protection across failure modes (corruption, accidental deletion, regional failure, cyber incidents).

Designing database systems that span multiple geographic regions for Meta's global scale. Understanding read replicas, write masters, data replication strategies, consistency models across regions, handling replication lag, and geographic failover mechanisms.

Tracking data movement, transformations, and usage across systems. Building tools and processes to answer questions like: where does this data come from, where does it go, and what systems would be impacted by changes? Integration with data catalogs and governance tools.

Building data stewardship programs that distribute governance responsibility across teams. Training users on policies, making compliance accessible, and embedding governance into development workflows rather than creating friction.

Understanding GDPR, CCPA, HIPAA, SOC 2, and other regulatory requirements. Designing systems and processes for compliance: audit logging, data retention policies, right-to-be-forgotten, data portability, and incident response.

Designing tiered data classification schemes (public, internal, confidential, restricted) based on sensitivity and regulatory requirements. Building governance frameworks that define handling requirements for each data tier, including encryption, retention, and access controls.

Designing and implementing encryption strategies for stored data (transparent data encryption, encrypted backups) and data in motion (TLS/SSL for client connections, encrypted replication). Managing encryption keys, key rotation, and hardware security modules.

Implementing role-based access control (RBAC), attribute-based access control (ABAC), row-level security, column-level masking, and principle of least privilege. Managing user permissions, access request workflows, and periodic access reviews.

Identifying repetitive manual tasks and automating them (backups, failover testing, compliance checks, performance reports). Building tools and scripts that reduce operational toil and free time for strategic improvements.

Analyzing data growth trends, predicting when capacity limits will be reached, planning scaling events well in advance, and preventing surprise capacity crises. Understanding cost implications of growth and optimization vs. scaling trade-offs.

Real-time optimization when systems are under stress. Techniques for rapid diagnosis and mitigation: query optimization, connection management, temporary resource allocation, and knowing when to fail gracefully vs. fight for the query.

Designing monitoring strategies that provide full visibility into database health: application-level metrics (latency, throughput, errors), database-level metrics (connection usage, query performance, replication lag), infrastructure metrics (CPU, memory, I/O), and business metrics (transactions per second, data growth rate).

Designing alert thresholds and rules that catch real problems early without generating alert fatigue. Multi-layered alerting (thresholds, anomaly detection, composite alerts), integration with on-call systems, and ensuring alerts reach the right people with actionable information.

Structured incident response: rapid diagnosis and mitigation, communication during incidents, escalation procedures, and recovery to normal state. On-call support structures: runbooks, escalation paths, blameless post-mortems, and knowledge sharing after incidents.

Explaining complex database concepts to non-technical stakeholders, managing expectations about what's possible and timeline, providing clear status during incidents, and writing documentation that enables others to be self-sufficient.

Examples of mistakes you've made, how you've learned from them, and improvements implemented to prevent recurrence. Comfort with admitting limitations and asking for help. Continuous learning attitude toward new database technologies and approaches.

Teaching database concepts to junior DBAs or engineers early in their careers. Providing constructive feedback, helping them grow skills, and building a stronger team through knowledge sharing and professional development.

Working effectively with developers, application teams, and product managers. Making database expertise accessible to non-specialists, helping teams make sound design decisions, providing timely support, and building trust through reliability.

Driving technical decisions through persuasion, not authority. Building consensus on database architecture choices, securing stakeholder buy-in for infrastructure investments, and championing reliability and security when facing time pressure.