Interview Formats & Rounds

Data engineering interviews follow a structured process with distinct rounds. Understanding what to expect helps you prepare effectively.

Typical Interview Pipeline

Phone Screen → Technical Screen → Onsite/Virtual Loop → Offer
   (30-45m)      (60-90m)           (4-6 hours)

Interview Round Types

1. SQL Assessment (Most Common)

The cornerstone of data engineering interviews—expect this in 90%+ of processes.

Difficulty	Topics	Time
Easy	Joins, GROUP BY, filtering	15-20 min
Medium	Window functions, CTEs, subqueries	30-45 min
Hard	Recursive CTEs, query optimization, execution plans	45-60 min

Format Variations:

• Live coding: Write queries in shared editor with interviewer
• Take-home: Complete SQL assessment asynchronously (1-3 hours)
• Whiteboard: Explain query logic without execution

Example Question:

-- Find the top 3 products by revenue per category,
-- including running total and rank

2. Coding Round (Python/Scala)

Tests data manipulation and algorithmic thinking.

Focus Area	Examples
Data processing	Parse JSON, transform records
File handling	Process large files efficiently
API integration	Fetch and aggregate API data
Testing	Write unit tests for pipelines

Common Patterns:

• Data transformation functions
• Batch processing logic
• Error handling and retries
• Memory-efficient processing

3. System Design

Design data systems at scale—increasingly important for senior roles.

Scope	Example Problems
Pipeline Design	"Design a real-time analytics pipeline"
Data Modeling	"Design the data model for an e-commerce platform"
Architecture	"Design a data lake for a 10TB/day ingestion"
Integration	"Design a CDC system for database replication"

Evaluation Criteria:

• Requirements gathering
• Component selection and trade-offs
• Scalability considerations
• Failure handling
• Cost awareness

4. Technical Deep-Dive

Discussion of specific technologies and past experience.

Topic Areas	Sample Questions
Spark	"Explain shuffle operations and optimization"
Airflow	"How do you handle DAG failures and retries?"
Warehousing	"Compare Snowflake vs. Redshift architecture"
Streaming	"Explain Kafka consumer groups and offsets"

5. Behavioral Round

Assesses culture fit and soft skills.

Theme	Example Questions
Collaboration	"Tell me about working with a difficult stakeholder"
Problem-solving	"Describe a complex data quality issue you solved"
Leadership	"How did you influence a technical decision?"
Failure	"Tell me about a pipeline that failed in production"

Company-Specific Formats

FAANG/Big Tech

Company	Known For
Meta	Heavy SQL focus, scale problems
Google	System design emphasis, coding rigor
Amazon	Leadership principles, practical scenarios
Netflix	Culture fit, senior-level expectations
Apple	Domain expertise, security awareness

Unicorns/Scale-ups

Company	Known For
Databricks	Spark deep-dive, distributed systems
Snowflake	SQL mastery, warehouse internals
Airbnb	Data modeling, business context
Stripe	Data quality, payment domain

Startups

Characteristics
Shorter process (2-3 rounds)
Practical take-home assignments
Emphasis on breadth over depth
Culture fit weighted heavily

Time Allocation Strategy

For a 4-hour onsite loop:

Round	Duration	Preparation Priority
SQL	60 min	HIGH - Practice daily
Coding	60 min	MEDIUM - LeetCode medium
System Design	60 min	HIGH - Study patterns
Behavioral	45 min	MEDIUM - Prepare stories
Hiring Manager	30 min	LOW - Be yourself

Red Flags to Watch For

In the Process:

• No SQL round (role may not be technical enough)
• Excessive rounds (>6 indicates dysfunction)
• No system design (limited growth potential)

In the Role:

• "We don't have documentation"
• "You'll figure it out as you go"
• "Our stack is completely custom"

Interview Tip: Ask your recruiter for the exact interview format upfront. Most will share the structure, allowing you to prepare specifically for each round.

Next, we'll explore what different company types expect from candidates. :::