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Meta Database Administrator (Staff Level) - Comprehensive Interview Preparation Guide

Database Administrator
Meta
Staff
7 rounds
Updated 6/15/2026

Meta's interview process for Staff-level Database Administrators typically consists of an initial recruiter screening call, two technical phone screens (one focused on database systems and one on system design), and four to five onsite interview rounds covering technical depth, system design, operational excellence, data governance, and leadership/cultural fit. The entire process spans 4-8 weeks and evaluates candidates on their ability to design and maintain mission-critical database systems at scale, make sound architectural decisions, and provide strategic guidance to engineering teams.

Interview Rounds

1

Recruiter Screening

2

Technical Phone Screen - Database Systems and Operations

3

Technical Phone Screen - System Design and Architecture

4

Onsite Round 1 - Database Design and Implementation

5

Onsite Round 2 - System Design: High Availability and Resilience

6

Onsite Round 3 - Data Governance, Performance, and Operational Excellence

7

Onsite Round 4 - Leadership, Influence, and Meta Cultural Fit

Frequently Asked Database Administrator Interview Questions

Data Modeling and Schema DesignHardTechnical
34 practiced
A BI query needs to compute median order value per customer from a large fact table. The DB lacks a native median aggregate and computing exact medians is expensive. Propose approximate schema or precomputation strategies to support both approximate and exact medians on demand.
Data Modeling and Schema DesignMediumSystem Design
36 practiced
Given a denormalized reporting table that is periodically rebuilt from source systems, how would you design incremental rebuilds to minimize downtime and keep the table consistent for readers? Include transactional considerations and schema-level techniques.
Data Modeling and Schema DesignMediumTechnical
39 practiced
Explain columnar vs row-oriented storage models. For which types of analytical queries is columnar storage significantly better, and why?
Data Modeling and Schema DesignEasyTechnical
44 practiced
What is database normalization aimed to prevent? List three common anomalies that normalization addresses and give a short example of each.
Data Modeling and Schema DesignMediumSystem Design
41 practiced
Design a schema and indexing strategy for a multi-tenant SaaS application's events table where tenants run queries scoped to their tenant_id and event_time ranges. Discuss partitioning, indexing, and schema choices that support both tenant isolation and efficient queries.
Data Modeling and Schema DesignEasyTechnical
43 practiced
Given a table of customer addresses, what schema choices would you make to support quick lookups by postal code, by city, and by geolocation (latitude/longitude)? Mention indexes and data types.
Data Modeling and Schema DesignHardSystem Design
32 practiced
A global company needs to shard its customer table across regions. Propose a logical sharding key and schema-level strategies for joins with orders that reference customers across shards. Discuss handling cross-shard transactions and referential integrity.
Data Modeling and Schema DesignMediumTechnical
54 practiced
A data pipeline writes to a warehouse where fact and dimension tables are stored in a columnar format. The team needs to support fast lookups of a small subset of rows (point selects) as well as large scans. What schema and physical design choices reduce latency for point selects without harming scan performance?
Data Modeling and Schema DesignMediumTechnical
56 practiced
Provide a step-by-step approach to evaluate and improve an index strategy on a production database with frequent schema changes. Include how you'd collect evidence, prioritize indexes, and safely deploy index changes.
Data Modeling and Schema DesignMediumTechnical
35 practiced
A legacy schema stores orders with denormalized customer and product details directly in the orders table to simplify reads. The table has grown to billions of rows and writes are slowing. What step-by-step strategy would you propose to evaluate and refactor the schema for both performance and maintainability?

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