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Data Engineering Interview Guide: Data Engineering Interview Prep Kit

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Data Engineering Interview Prep Kit

Comprehensive preparation for data engineering interviews at top tech companies. Covers SQL challenges, ETL/ELT pipeline design, data modeling (Kimball & Data Vault), and system design for data-intensive applications. Based on real interview loops at FAANG, unicorns, and high-growth startups.

Key Features

  • 60 SQL challenges graded Easy → Expert with solutions and query plans
  • 15 ETL pipeline design problems with architectural diagrams
  • Data modeling exercises covering star schema, Data Vault 2.0, and activity schema
  • 8 full system design scenarios for data platforms
  • Take-home project templates with evaluation criteria
  • Interviewer rubrics — know exactly what's being scored

Content Breakdown

Section Items Difficulty Range
SQL Challenges 60 ★ to ★★★★★
Pipeline Design 15 ★★ to ★★★★
Data Modeling 12 ★★ to ★★★★
System Design 8 ★★★ to ★★★★★
Take-Home Projects 5 ★★★
Behavioral (DE-specific) 20 ★★

Sample Content

SQL Challenge: Window Functions (Medium)

Problem: Given an orders table, find each customer's order amount and the running average of their last 3 orders.

-- orders(order_id, customer_id, order_date, amount)

SELECT
    customer_id,
    order_date,
    amount,
    ROUND(AVG(amount) OVER (
        PARTITION BY customer_id
        ORDER BY order_date
        ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
    ), 2) AS running_avg_3
FROM orders
ORDER BY customer_id, order_date;
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Follow-up questions interviewers ask:

  1. What if we want RANGE instead of ROWS? When does it matter?
  2. How would you handle ties in order_date?
  3. What's the performance impact on 100M rows? How would you optimize?

Pipeline Design Problem: Real-Time Event Analytics

Prompt: Design an analytics pipeline that ingests 50K events/second from a mobile app, stores raw events for replay, and serves real-time dashboards with <5 second latency.

Expected Architecture:

Mobile App → API Gateway → Kafka (raw events topic)
                              │
              ┌───────────────┼───────────────┐
              ▼               ▼               ▼
        S3 Raw Archive   Flink/Spark    Dead Letter Queue
        (Parquet, partitioned  Streaming
         by date/hour)        │
                              ▼
                    Aggregation Store
                    (Redis / Druid)
                              │
                              ▼
                    Dashboard API
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Key discussion points:

  • Schema evolution strategy (Avro + Schema Registry)
  • Exactly-once vs at-least-once semantics
  • Backpressure handling when Kafka consumers fall behind
  • Cost comparison: Flink vs Spark Structured Streaming
  • Late-arriving data and watermark strategies

Data Modeling: Slowly Changing Dimension Type 2 

-- SCD Type 2: Track customer address history
CREATE TABLE dim_customer (
    customer_sk     BIGINT GENERATED ALWAYS AS IDENTITY,
    customer_id     VARCHAR(50),    -- natural/business key
    name            VARCHAR(200),
    email           VARCHAR(200),
    city            VARCHAR(100),
    state           VARCHAR(50),
    effective_date  DATE NOT NULL,
    end_date        DATE,           -- NULL = current record
    is_current      BOOLEAN DEFAULT TRUE,
    PRIMARY KEY (customer_sk)
);

-- Interview question: "How do you handle a retroactive correction
-- vs a legitimate address change?"
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Study Plan

Week Focus Daily Time
1 SQL fundamentals → window functions → CTEs 60 min
2 Advanced SQL: recursive CTEs, query optimization, EXPLAIN plans 60 min
3 Data modeling: star schema, SCD types, Data Vault basics 45 min
4 Pipeline design: batch architecture patterns 45 min
5 Pipeline design: streaming architecture patterns 45 min
6 System design: full data platform scenarios 60 min
7 Take-home project practice 90 min
8 Mock interviews + review weak areas 60 min

Practice Tips

  1. SQL is non-negotiable. Every DE interview includes SQL. Aim to solve medium problems in 10 minutes.
  2. Draw architectures, don't just describe them. Use the included diagram templates.
  3. Know your tradeoffs. Batch vs streaming, Kimball vs Data Vault, Parquet vs Delta — argue both sides.
  4. Prepare a "pipeline war story." One detailed story about a pipeline you built, debugged, or optimized.
  5. Practice cost estimation. "How much would this pipeline cost to run on AWS at 10TB/day?"

Contents

  • src/ — SQL challenges, modeling exercises, pipeline design problems
  • examples/ — Complete solutions with architectural diagrams
  • docs/ — Interviewer rubrics, study guides, tool comparison matrices

This is 1 of 11 resources in the Interview Prep Pro toolkit. Get the complete [Data Engineering Interview Guide] with all files, templates, and documentation for $39.

Get the Full Kit →

Or grab the entire Interview Prep Pro bundle (11 products) for $199 — save 30%.

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