Interviews Analyzed
3,200
What data engineers are actually asked in interviews.
Analysis of 3,200+ data engineer interviews across top tech companies, fintech, and startups.
Average Prep Time
12weeks
foundations
deep
system
polish
Offers Landed
72%
Among candidates following the plan
Avg Salary Bump
+$38k
Pre-offer vs post-offer base + equity
We pull jobs from sources most job seekers never check
01 — Companies
What top companies emphasize.
Interview focus varies significantly between company types and their data infrastructure needs.
FAANG
HighMeta · Google · Amazon · Netflix · Apple
100%
- Algorithms 35%
- System design 45%
- Behavioral 20%
Heavy emphasis on distributed systems design and scalability challenges at petabyte scale.
Distributed SystemsScale
FINTECH
Medium-HighStripe · Square · Coinbase · Robinhood · Plaid
100%
- Algorithms 25%
- System design 40%
- Behavioral 25%
- Domain / fit 10%
Focus on data accuracy, compliance, and real-time financial data processing.
Data QualityCompliance
EARLY-STAGE · SERIES A-B
MediumVarious startups · Scale-ups
100%
- Algorithms 20%
- System design 30%
- Behavioral 25%
- Domain / fit 25%
Emphasis on building data infrastructure from scratch and wearing multiple hats.
Full-Stack DataOwnership
02 — Topics
Most frequently tested topics
68% of interviews containing topic
01
SQL & Database Design
85%joins, indexing, query optimization, normalization, ACID
Complex queries, performance tuning, and schema design fundamentals
02
Data Pipeline Architecture
78%ETL, streaming, batch processing, orchestration, monitoring
End-to-end pipeline design with fault tolerance and scalability
03
Distributed Systems
72%consistency, partitioning, replication, CAP theorem, consensus
Understanding trade-offs in large-scale distributed data systems
04
Stream Processing
65%Kafka, event sourcing, windowing, backpressure, exactly-once
Real-time data processing patterns and streaming frameworks
05
Data Modeling
58%dimensional modeling, star schema, data vault, lakehouse, governance
Warehouse design patterns and modern data architecture approaches
06
Python & Algorithms
52%data structures, sorting, graph algorithms, dynamic programming, complexity
Core programming skills with focus on data processing algorithms
03 — Interview loop
Typical interview process
System design rounds are often the bottleneck, requiring deep understanding of data architecture patterns and trade-offs.
Pass-rate funnel
Phone Screen · 78%
Technical Coding · 65%
System Design · 42%
Domain Deep Dive · 58%
Behavioral · 72%
Offer rate compounded ≈ 1.3%
01
Phone Screen
45 min · pass 78%
SQL problems and basic data engineering concepts
02
Technical Coding
60 min · pass 65%
Python/SQL coding with data processing focus
03
System Design
BOTTLENECK60 min · pass 42%
Design data pipelines, storage systems, or analytics platforms
04
Domain Deep Dive
45 min · pass 58%
Data modeling, ETL design, or specific technology discussion
05
Behavioral
45 min · pass 72%
Leadership, collaboration, and technical decision-making
04 — Question bank
Real questions you'll encounter.
Curated from actual data engineer interviews at top companies
SQL & QUERIES
Medium → HardComplex joins
- window functions
- recursive CTEs
- query optimization
- index design
DATA PIPELINES
Medium → HardETL design
- batch vs streaming
- error handling
- data quality
- monitoring
SYSTEM DESIGN
HardData warehouse
- analytics platform
- real-time dashboard
- recommendation system
- log aggregation
STREAMING
Medium → HardEvent processing
- Kafka architecture
- stream joins
- windowing
- exactly-once delivery
PYTHON & ALGORITHMS
MediumData processing
- merge datasets
- deduplication
- time series analysis
- graph traversal
ARCHITECTURE
HardDistributed systems
- data partitioning
- consistency models
- fault tolerance
- scaling strategies
850 questions in the bank
Open the full bank →05 — Prep roadmap
12-week preparation roadmap
Structured approach to mastering data engineering interviews, from SQL fundamentals to distributed system design.
Hours / week
Total: 78 hrs
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
Weeks 1-3
5 hrs/wk
SQL & Python Foundations
Master SQL query optimization, Python data processing, and core algorithms. Build strong fundamentals in data structures and database concepts.
SQLPythonAlgorithmsDatabases
Weeks 4-7
7 hrs/wk
Data Pipeline Engineering
Learn ETL design patterns, batch and stream processing frameworks, data quality, and pipeline orchestration tools.
ETLAirflowSparkData Quality
Weeks 8-10
8 hrs/wk
System Design Mastery
Practice designing data warehouses, real-time analytics platforms, and distributed data systems. Focus on scalability and trade-offs.
System DesignArchitectureScalabilityTrade-offs
Weeks 11-12
7 hrs/wk
Mock Interviews & Polish
Simulate real interview conditions, refine communication skills, and practice explaining complex technical decisions clearly.
Mock InterviewsCommunicationBehavioralFinal Prep
06 — Tools & resources
Tools & resources that work.
Battle-tested by candidates who landed offers.
Mix of free + premium.
InterviewPal
Guided interview prep with mentorship and structured paths.
Best for: Structured prep
Visit InterviewPal
LeetCode
2,000+ coding problems. Premium unlocks company-tagged sets.
Best for: Algorithms & DS
Visit LeetCode
System Design Primer
Free comprehensive guide. The de-facto starting point.
Best for: SD fundamentals
Visit System Design Primer
Blind
Anonymous tech community. Real interview experiences and insights.
Best for: Real signal
Visit Blind
Levels.fyi
Salary and interview data, by company and level.
Best for: Company intel
Visit Levels.fyi
Pramp
Peer mock interviews. Live practice with real people.
Best for: Live practice
Visit PrampFrequently Asked Questions
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