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Meta Database Administrator (Mid-Level) Interview Preparation Guide

Database Administrator
Meta
Mid Level
7 rounds
Updated 6/18/2026

Meta's interview process for mid-level Database Administrators typically follows a structured evaluation across multiple rounds designed to assess technical depth in database management, system design thinking, operational excellence, data governance understanding, and cultural fit. The process begins with a recruiter screen, progresses through technical phone interviews focused on core database skills, and culminates in multiple onsite rounds covering hands-on technical challenges, large-scale system design, data governance and security, and behavioral assessment.

Interview Rounds

1

Recruiter Screening

2

Technical Phone Screen – Database Design and SQL

3

Technical Phone Screen – Performance Tuning and Operations

4

Onsite Round 1 – Core Database Administration and Technical Problem Solving

5

Onsite Round 2 – System Design and High Availability Architecture

6

Onsite Round 3 – Data Governance, Security, and Compliance

7

Onsite Round 4 – Behavioral Interview and Cultural Fit

Frequently Asked Database Administrator Interview Questions

Data Modeling and Schema DesignHardTechnical
34 practiced
Design a schema and approach for storing event attributes where events have a semi-structured set of key-value attributes that vary per event type. Discuss trade-offs between EAV (entity-attribute-value), JSONB/VARIANT columns, and separate typed tables in terms of queryability, indexing, and schema evolution.
Data Modeling and Schema DesignMediumTechnical
38 practiced
Given this simple schema for product reviews:
reviews(review_id, product_id, user_id, rating, comment, created_at)
A customer asks for a leaderboard of top 10 products by average rating in the last 30 days. Propose schema-level changes or indexes to make this query fast under heavy write load, explaining your choices.
Data Modeling and Schema DesignEasyTechnical
36 practiced
Define grain in dimensional modeling. Provide three examples of different fact table grains for an online retail business and explain one practical consequence of choosing a too-coarse or too-fine grain.
Data Modeling and Schema DesignMediumTechnical
30 practiced
Design a dimension table for 'product' to be used in a data warehouse where products can change category and price frequently. Explain how you would handle SCD for category and price, and what columns you'd include to support effective-dated queries.
Data Modeling and Schema DesignEasyTechnical
31 practiced
You are designing a transactional database for a small e-commerce application. Describe the core tables and their relationships (orders, customers, products, order_items, payments, shipments). What normalization level would you apply initially and why? Include primary keys and foreign keys in your description.
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 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 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
29 practiced
Create a migration plan (high level) to convert a busy orders table from storing full addresses inline to referencing an addresses table (normalization), ensuring no downtime and minimal risk. Include steps for data migration, application compatibility, and rollback.
Data Modeling and Schema DesignHardSystem Design
32 practiced
You are asked to design a schema for a real-time analytics dashboard that needs near-real-time metrics (within seconds) and supports ad-hoc drilldowns. Outline a hybrid architecture and schema choices to meet low-latency ingestion and flexible querying.

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