Learn how to prepare for data engineering interviews and understand the set of skills expected from a data engineer.

Data Engineering

What is Data Engineering?

Data Engineering Skills

Data Engineering Interview Preparation

Data Engineering Learning Path Overview

Introduction to Data Engineering

Medium level SQL questions utilize more advanced concepts like sub-queries, window functions, and solving case study problems.

Introduction to medium SQL questions

DATETIME

Self-JOINs and CROSS JOINs

Subqueries and CTEs

Daily Logins

Employee Salaries (ETL Error)

Slacking Employees Salaries

CASE clause

Exam Scores

Customer Orders

Employee Salaries (Top 3 departments)

Handling NULL values

Empty Neighborhoods

Distance Traveled

LIKE and other useful functions

Window Functions

Last Transaction

Top Three Salaries

User Experience Percentage

Medium SQL Questions

Data structures in Python attempt to be more intuitive and flexible than traditional data structures in other programming languages.

Introduction

Basic Data Types

Iterables

Functions and Classes

Find the Missing Number

Find Bigrams

Merge Sorted Lists

Permutation Palindrome

Equivalent Index

Data Structures

Learn the dimensional modeling framework for designing data warehouses.

Introduction to Dimensional Modeling

Dimension And Fact Tables

Snowflake vs. Star Schemas

Dimensional Modeling Framework

Retailer Data Warehouse

Ride Sharing App Schema

Dimensional Modeling

Let's tackle advanced SQL interview questions that focus on multi-joins and layers of data interpretation. These questions may come up in take-home challenges and senior level interviews.

Introduction to hard SQL questions

Swipe Precision

Cumulative Distribution

Rolling Bank Transactions

Subscription Overlap

First Touch Attribution

Search CTR

Paired Products

Payments Received

Hard SQL Questions

Let's try some hard Python questions that you would see in tougher data science interviews and many machine learning interviews.

Max Width

Scrambled Tickets

Bernoulli Sample

Sum to N

Check Normality

Pool Matching

Python Questions: Hard

Data processing and analysis is the first step that we need to consider once we've clarified details and started down the path of building the model.

Overview

Missing Housing Data

Variable Error

Skewed Pricing

Bank Fraud Model

Data Pre-Processing

Learn a framework to ace database system design interview questions. Practice defining the problem clearly, justifying your design decisions, and considering edge cases.

Introduction to Database Design

What is Database Design?

Database System Design Framework

Identifying Database Requirements

Prioritizing Database Requirements

Case study: End-User Oriented Solution

Swipe Payment API

Subway Machine Learning Model

Relational vs. Non-Relational Databases

Choosing a Database Management System

Common Edge Cases in Database Design

International e-Commerce Warehouse

Data Pipelines and Aggregation

Clickstream Data

Database System Design

Data engineers develop the tools and frameworks needed for data teams to function: they build pipelines, manage ETL, and build scalable data infrastructures. Learn the skills evaluated in data engineering job interviews.

Learn how to tackle data engineering job interviews.

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

The dataset is imbalanced and precision matters the most

The dataset is imbalanced and recall matters the most

The dataset is imbalanced and precision and recall matter equally

The dataset is balanced and precision matters the most

The dataset is balanced and recall matters the most

The dataset is balanced and precision and recall matter equally

Data Engineering

Bank Fraud Model
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31%

Data Engineering

Bank Fraud ModelGo to question details page

31%

Bank Fraud Model
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