Top 20 Apple Data Engineer Interview Questions + Guide in 2024

Apple generated $383 billion in revenue last year alone as the first publicly traded company to reach a market capitalization of a trillion dollars.

Apple’s consumer-centric business model relies heavily on system-wide collaborations and data collection to improve customer experience. The data engineers at Apple help develop the systems for storing, aggregating, maintaining, and analyzing large amounts of data.

Given that only 0.5% of applicants progress to the hiring stage at Apple, you’ve come to the right place to prepare with our comprehensive interview guide for the data engineer position.

We’re here to guide you through the interview process, answer common Apple data engineer interview questions, and share tips to help you ace it.

Apple seeks candidates with multiskilling abilities, substantial experience, and cultural alignment. Expect to undergo a stringent interview process for the data engineer role at Apple.

The interviewers will evaluate your technical and behavioral competency through a 5-stage process comprising two virtual interviews, a take-home assignment, and an on-site interview round.

Application Submission

Apple recruiters rarely reach out before receiving your application. We suggest you go through the Apple Carrer Portal to find suitable data engineer positions to apply for by submitting your CV. Emphasize your experience in the required domains outlined in the essential qualifications and job description associated with the job posting.

First Recruiter Call

The shortlisted CVs will receive an initial confirmation within a few days of applying. If you find yourself among the select few, the recruiter will schedule another call to verify your basic details and ensure you would be a good fit for the company. Like other tech companies, they might ask basic behavioral questions and a few pre-screening technical questions related to the position.

A second 15 to 30-minute phone call with your hiring manager may also be arranged—an opportunity to make a solid first impression.

Technical Phone Screening

After success in the recruiting stage as a data engineer candidate at Apple, you will be invited to the technical phone screening interview.

Here, you’ll attend multiple FaceTime interviews with senior data engineers and possibly your hiring manager. During these interview rounds, you’ll be asked demanding behavioral questions and technical questions related to data engineering, including SQL, Python, and algorithms.

Take-Home Assessment

If you pass the previous rounds, you’ll be given a take-home assignment to submit within up to 72 hours. For the Apple data engineer interview, datasets are usually provided, and candidates are asked to write an SQL query to solve a real-world ETL or data quality issue.

However, most new graduates are asked to appear at the Apple Assessment Center to complete the assignments, which include written and group exercise rounds.

On-Site Interview Rounds

Multiple one-on-one rounds will be conducted during the Apple On-Site Interview Loop. Expect each round to take roughly 45 minutes and more than 6 hours for the whole process to conclude.

You’ll be thoroughly evaluated on your knowledge of building data pipelines, managing complex data projects, behavioral alignment, coding prowess, and more. Traditionally, candidates also meet the hiring manager and the prospective team over lunch to discuss their domain knowledge.

If you’ve made a good impression, you’ll be offered the data engineer position at Apple through a call or email. Your recruiter will also guide you through the remaining pre-employment checks and signing the documents.

Apple expects its data engineer candidates to have strong programming skills, in-depth knowledge of SQL data querying, and technical soundness in big data technologies. An analytical understanding of data wrangling, pipelines, and warehousing is a plus.

It’s also critical to know Apple’s culture and the latest technologies to tackle intimidating behavioral questions efficiently.

We gathered real Apple data engineer interview questions and designed the responses to give you an idea of how to answer them.

1. Why did you apply to our company?

Your understanding of Apple’s values, mission, and culture will be assessed with this question. It also evaluates your alignment with the specific role and your motivation to work for Apple.

How to Answer

Highlight aspects of Apple’s culture, products, and impact that resonate with you. Discuss how your skills and career goals align with the company’s mission and vision.

Example

“I applied to Apple because I admire the company’s commitment to innovation and excellence in products and services. As a data engineer, I’m particularly drawn to Apple’s emphasis on using data to enhance user experiences and drive decision-making. I’m excited about the opportunity to contribute to cutting-edge projects and be part of a team that pushes boundaries in technology.”

2. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

This question evaluates your problem-solving skills, ability to take initiative, and willingness to go above and beyond in your work.

How to Answer

Choose a specific project where you faced challenges or tight deadlines. Describe your actions to overcome the obstacles, your strategies, and the outcome of your efforts.

Example

“During a recent data migration project, we encountered unexpected issues that threatened to delay the timeline. Recognizing the urgency, I identified alternative solutions, streamlined processes, and collaborated closely with cross-functional teams to speed the project along. Using automation tools and optimizing workflows, we met the deadline and improved data accuracy and efficiency, which exceeded expectations.”

3. How do you prioritize tasks and stay organized when you have multiple deadlines?

The interviewer at Apple may ask this to ensure you can handle multiple projects efficiently as a data engineer, which is critical for the dynamic nature of the position.

How to Answer

Discuss a systematic approach to prioritizing deadlines, such as assessing the urgency and impact of each task, breaking down tasks into smaller steps, and leveraging tools like project management software or to-do lists to stay organized.

Example

“When faced with multiple deadlines, I first consider the urgency and impact of each task. I prioritize tasks based on their deadlines and importance to the overall project. To stay organized, I break down each task into smaller, manageable steps and create a detailed timeline using project management software like Jira. This allows me to track my progress and ensure that I meet each deadline effectively.”

4. Describe a data quality issue you experienced within a large dataset. How did you approach troubleshooting the issue?

Handling real-world challenges and ensuring data accuracy is key to being successful as an Apple data engineer. This question evaluates your problem-solving skills and attention to detail when dealing with data quality issues.

How to Answer

Describe a data quality issue you encountered, your steps to identify the root cause, and the strategies you implemented to resolve the issue. Highlight your analytical skills, attention to detail, and ability to collaborate with team members to troubleshoot and prevent similar issues in the future.

Example

“In a previous project, I encountered a data quality issue where duplicate records were affecting the accuracy of our analysis. I began by conducting a thorough data audit to identify the extent of the issue and traced it back to a data ingestion pipeline error. I worked with the data engineering team to fix the pipeline issue and implemented data validation checks to prevent duplicate records from entering the system. This proactive approach resolved the immediate issue and improved our system’s overall data quality and reliability.”

5. Tell us about a time you collaborated with other teams on a data project. How did you ensure clear communication and successful collaboration to achieve the project goals?

This question evaluates your communication and teamwork skills, essential for collaborating with cross-functional teams in Apple’s work environment.

How to Answer

Describe a specific data project on which you collaborated with other teams and achieved the project goals, highlighting your ability to foster clear communication, resolve conflicts, and leverage team member’s strengths.

Example

“In a recent data project, I collaborated with the marketing and product development teams to analyze customer engagement metrics and identify opportunities for product optimization. To ensure clear communication and successful cooperation, I organized regular cross-functional meetings to align project objectives, share insights, and discuss action plans. I also created detailed documentation to outline project milestones, responsibilities, and deadlines to ensure everyone was on the same page. By tapping into each team member’s expertise and fostering a collaborative environment, we achieved our project goals ahead of schedule and drove significant improvements in customer engagement.”

6. Say you are tasked with designing a data mart or data warehouse for a new online store. How would you design the system?

Note: Sketch a star schema to explain your design.

Apple’s online stores are vital to its operations as a consumer brand. The interviewer may ask this to understand your approach as a data engineer to structuring data for analysis and reporting in the context of an e-commerce platform like their online store.

How to Answer

Describe your approach and sketch a star schema diagram representing the fact and dimension tables, explaining how you would organize data related to products, orders, customers, and sales.

Example

“First, the task is to design a data warehouse for a new online retailer. From what I understand, it’s mainly for analytics and reporting purposes, focusing on sales data. To start, I need to identify the business process, which involves designing a star schema. Star schemas are efficient for querying, hinting that the warehouse is for analytics—assuming the events stored are sales data. Next, I would declare the granularity. Each distinct product sale is considered an event, but multiple items of the same product in one transaction count as one event. Then, I’ll identify the dimensions. For “WHO,” it’s about the buyer, so attributes like customer ID, name, city, etc. are crucial. “WHAT” includes details about the item sold, like name, brand, etc. “WHEN” involves storing dates at various granularities. “HOW” relates to payment and promotions.

Finally, there are facts to consider. Each fact corresponds to a sale and includes quantity sold, total amount paid, total cost, and net revenue. So, it’s about setting up a warehouse that efficiently handles sales data, focusing on key dimensions and facts for analysis and reporting.”

Star Schema (source):

7. Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half.

If there is no index where this condition is satisfied, return -1.

Example 1:

Input:

nums = [1, 7, 3, 5, 6]

Output:

equivalent_index(nums) -> 2

Example 2:

Input:

nums = [1,3,5]

Output:

equivalent_index(nums) -> -1

This question assesses your problem-solving skills and understanding of array manipulation.

How to Answer

Write a function that iterates through the array, calculating the sum of elements on the left and right sides, and returns the index where the sums are equal.

Example

def equivalent_index(nums):
    total = sum(nums)
    leftsum = 0
    for index, x in enumerate(nums):
        # the formula for computing the right side
        rightsum = total - leftsum - x
        leftsum += x
        if leftsum == rightsum:
            return index
    return -1

8. Given a table called employees, get the largest salary of any employee by department.

Example:

Input:

employees table

Column	Type
id	INTEGER
department	VARCHAR
salary	INTEGER

Output:

Column	Type
department	VARCHAR
largest_salary	INTEGER

You’ll frequently write SQL queries to retrieve data and manipulate databases as a data engineer at Apple. The interviewer may ask this question to evaluate your ability to write complex SQL queries to retrieve specific information from a database.

How to Answer

Write an SQL query that retrieves the largest salary for each department from the “employees” table.

Example

SELECT
  department,
  MAX(salary) AS largest_salary
FROM employees
GROUP BY department

9. You’re in charge of forecasting revenue for a certain target over the next quarter for a certain company.

Given three parameters of:

• N days (N)
• Total Revenue Target (XYZ)
• Day 1 Revenue (day_one_rev)

How would you build a function to return a list of daily forecasted revenue starting from Day 1 to the end of the quarter (Day N)?

Note: The company’s product is expected to have continual linear growth from Day 1 to Day N, starting from its CURRENT average daily revenue number.

Your problem-solving skills and ability to develop forecasting algorithms will be assessed with this question. Apple may ask it to evaluate your approach to building predictive models.

How to Answer

Write a function that generates daily revenue forecasts based on the provided parameters, considering linear growth from day 1 to day N.

Example

def generate_forecasts(N, XYZ, day_one_rev):
    daily_growth = XYZ / N
    forecasts = [day_one_rev + (day * daily_growth) for day in range(1, N + 1)]
    return forecasts

10. Write a function ugly_powers(s: set) -> bool that takes a set s and returns a Boolean value determining whether or not all the elements of set s are all ugly powers.

Note: A Hamming number (also called an ugly number) is any positive integer that has its set of prime factors as a subset of the prime numbers 2, 3, and 5. On the other hand, a prime power is produced using any integer k raised to a prime p and is represented as p^k.

As such, we can create a theoretical term, an “ugly power,” which is any ugly number multiplied by any arbitrary positive integer k. We can represent this by having any ugly number ℎ and an ugly power be ℎ^k.

Can you write a solution that’s approximately O(nlog(n))?

Example:

ugly_powers({1, 2, 5, 10}) -> True

Domain knowledge of algorithms is critical as a data engineer employed at Apple. This question assesses your understanding of number theory and algorithmic efficiency.

How to Answer

Write a function that checks whether all elements of a set are ugly powers, using an algorithm with approximately O(nlog(n)) time complexity.

Example

def is_ugly(n: int):
    while n != 1:

        if n% 3 == 0:
            n /= 3
            continue

        if n % 5 == 0:
            n /= 5
            continue

        if n % 2 == 0:
            n /= 2
            continue

        return False

    return True

def ugly_powers(s: set) -> bool:
    return all([is_ugly(x) for x in s])

11. Write a function named grades_colors to select only the rows where the student’s favorite color is green or red and their grade is above 90.

You’re given a dataframe of students named students_df:

students_df table

name	age	favorite_color	grade
Tim Voss	19	red	91
Nicole Johnson	20	yellow	95
Elsa Williams	21	green	82
John James	20	blue	75
Catherine Jones	23	green	93

Example:

Input:

import pandas as pd

students = {"name" : ["Tim Voss", "Nicole Johnson", "Elsa Williams", "John James", "Catherine Jones"], "age" : [19, 20, 21, 20, 23], "favorite_color" : ["red", "yellow", "green", "blue", "green"], "grade" : [91, 95, 82, 75, 93]}

students_df = pd.DataFrame(students)

Output:

def grades_colors(students_df) ->

name	age	favorite_color	grade
Tim Voss	19	red	91
Catherine Jones	23	green	93

Your interviewer for the data engineer role over at Apple may ask this question to evaluate your proficiency in data manipulation and filtering techniques.

How to Answer

Write a function that filters rows based on the specified conditions using pandas DataFrame operations.

Example

import pandas as pd

def grades_colors(students_df):
    students_df = students_df[(students_df['grade'] > 90) &
          students_df['favorite_color'].isin(['red','green'])]
    return students_df

12. You are given two non-empty linked lists representing two non-negative integers. Each list contains a single number, where each item in the list is one digit. The digits are stored in reverse order.

Task: Add the two numbers and return the sum as a linked list with the digits in reverse order. You may assume the two numbers do not contain any leading zeros except the number 0 itself.

Example 1:

Input:

l1 = 2->4->3->null
l2 = 5->6->4->null

Output:

addTwoNumbers(l1, l2) = 7->0->8->null

Explanation: 342 + 465 = 807.

Example 2:

Input:

l1 = 0->null
l2 = 0->null

Output:

addTwoNumbers(l1, l2) -> 0->null

Explanation: 0 + 0 = 0.

This question assesses your ability to work with linked lists and perform arithmetic operations, which are fundamental to data manipulation.

How to Answer

Write a function that traverses both linked lists, adds the corresponding digits, and handles carryover. Return the sum as a new linked list.

Example

class ListNode:
    def __init__(self, x):
        self.val = x
        self.next = None
def to_linked_list(nums):
    """Help function, do not modify"""
    """A helper function: Converts a list of integers to a linked list and returns the head of the linked list."""
    if not nums:
        return None
    head = ListNode(nums[0])
    current = head
    for num in nums[1:]:
        current.next = ListNode(num)
        current = current.next
    return head

def addTwoNumbers(nums1, nums2):
    l1 = to_linked_list(nums1)
    l2 = to_linked_list(nums2)
    dummy = cur = ListNode(0)
    carry = 0
    while l1 or l2 or carry:
        if l1:
            carry += l1.val
            l1 = l1.next
        if l2:
            carry += l2.val
            l2 = l2.next
        cur.next = ListNode(carry%10)
        cur = cur.next
        carry //= 10
    return dummy.next

13. Imagine you have a large table containing user app usage data. How would you use SQL queries to identify the most frequently used app features by users in a specific age group and location?

Handling data lakes and running queries on them would be integral to your job as a data engineer at Apple. Your interviewer may ask this question to evaluate your ability to extract insights from large datasets using SQL.

How to Answer

Write SQL queries that aggregate app usage data based on age group and location, identifying the most frequently used app features.

Example

SELECT feature, COUNT(*) AS usage_count
FROM app_usage
WHERE age_group = '18-25' AND location = 'California'
GROUP BY feature
ORDER BY usage_count DESC
LIMIT 5;

14. Explain data pipelines and how they are used in data engineering. How could you leverage Python libraries and frameworks to build a data processing workflow that ingests data from various sources, transforms it, and loads it to a data warehouse at Apple?

This question assesses your understanding of data engineering concepts and tools. Apple may ask this question to evaluate your knowledge of building data processing workflows and using Python libraries/frameworks for data ingestion, transformation, and loading.

How to Answer

Explain the concept of data pipelines and discuss how Python libraries and frameworks can be used to orchestrate data workflows, integrate with data processing tools, and load data into a data warehouse.

Example

“A data pipeline is a series of data processing steps that transform raw data into a usable format and load it into a target destination. In data engineering, pipelines are used to automate and streamline the data processing workflow. At Apple, we can leverage Python libraries like Apache Airflow or Luigi to build and manage data pipelines. These frameworks allow us to define tasks, dependencies, and scheduling for data processing tasks. We can use pandas for data manipulation and transformation, while SQLAlchemy can help interact with databases for data loading.”

15. Apple gathers data from various sources that often require cleaning and preparation before analysis. Describe some common Python libraries or techniques for handling missing values, data type conversions, and data formatting in data engineering pipelines.

As a data engineer, your knowledge of Python libraries and techniques for data cleaning and preparation will be evaluated through this question.

How to Answer

Describe common Python libraries, such as pandas, for handling missing values and data type conversions, and techniques, like regular expressions, for data formatting.

Example

“In data engineering pipelines, handling missing values, data type conversions, and data formatting is crucial to ensure accurate analysis. Python libraries like pandas provide functions such as fillna() and astype() to handle missing values and convert data types, respectively. Additionally, techniques like regular expressions can be used for data formatting tasks such as extracting patterns from strings. For example, the str.extract() function in pandas can be used with regular expressions to extract specific information from text data.”

16. Traditional algorithms might not be efficient when dealing with very large datasets. Explain the concept of MapReduce or Spark and how these frameworks can be used to process big data at Apple.

Apple may ask this question to evaluate your familiarity with frameworks and how you would use them to process large volumes of data effectively within their ecosystem.

How to Answer

Explain the concepts of MapReduce or Spark. Discuss how these frameworks can parallelize data processing tasks across clusters, enabling scalable and efficient processing of large datasets.

Example

“MapReduce and Spark are distributed computing frameworks designed for processing large datasets where data is divided into smaller chunks and processed in parallel across multiple nodes. They enable scalable and fault-tolerant data processing, essential for handling Apple’s vast amounts of user and device data.”

17. A table in your data warehouse experiences slow query performance for specific queries. How would you identify the bottleneck and suggest an appropriate indexing strategy to improve query speed?

The data engineer interviewer at Apple may ask this question to check your understanding of database indexing principles and problem-solving skills in improving query efficiency.

How to Answer

Explain how you would analyze query execution plans, identify potential bottlenecks such as full table scans or inefficient join operations, and propose appropriate indexing strategies to improve query performance.

Example

“To identify bottlenecks, I would examine query execution plans. If I observe full table scans or high disk I/O operations, it indicates potential performance issues. I would then analyze the WHERE clauses and join conditions to identify columns that could benefit from indexing. By creating indexes on frequently used columns, we can improve query performance and reduce disk I/O operations, enhancing overall data retrieval speed.”

18. Explain the difference between subqueries and views in SQL. When would you use each, and how can they benefit data analysis at Apple?

This question evaluates your understanding of SQL constructs and their applications in data analysis tasks.

How to Answer

Differentiate between subqueries and views, explaining when to use each construct and their benefits in simplifying query logic and promoting code reusability.

Example

“Subqueries are nested queries useful for performing operations on subsets of data, while views provide a layer of abstraction over complex SQL queries, promoting code reusability and enhancing query readability. At Apple, subqueries can be used to perform ad-hoc analysis or filter data dynamically within queries. At the same time, views can be employed to create logical data models, standardize reporting views, or enforce data access policies across different user roles.”

19. Apple products often recommend content or services to users. Briefly describe different types of recommendation algorithms and how they could be used to personalize user experiences at Apple.

Apple may ask this question to evaluate your knowledge of different recommendation techniques and how you would use them to enhance user experiences across Apple products and services.

How to Answer

Describe various recommendation algorithms, such as collaborative filtering and content-based filtering, and discuss their applications in delivering tailored content recommendations to users.

Example

“Recommendation algorithms like collaborative filtering and content-based filtering play a crucial role in personalizing user experiences by suggesting relevant content based on user preferences and behavior. At Apple, these algorithms can be applied across various platforms and services, such as Apple Music, App Store, and Apple TV+, to recommend music tracks, apps, movies, or TV shows tailored to individual users’ tastes and preferences, enhancing overall user engagement and satisfaction.”

20. Large datasets can be partitioned to improve processing speed. Explain data partitioning or sharding and how these strategies can benefit distributed data storage and retrieval at Apple.

This question assesses your knowledge of data partitioning strategies and their role in distributed data storage and retrieval.

How to Answer

Explain data partitioning or sharding, highlighting how they distribute data across multiple nodes or servers to improve parallelism, data locality, and system scalability. Discuss their benefits in facilitating distributed data storage and retrieval, particularly in scenarios involving large datasets or high transaction volumes.

Example

“Data partitioning or sharding involves dividing a large dataset into smaller partitions distributed across multiple nodes, improving system scalability and performance, essential for handling Apple’s vast amounts of user-generated data. Data partitioning can be beneficial at Apple for handling massive volumes of user-generated data, such as app usage logs, device telemetry, or multimedia content, enabling seamless scalability and high availability of Apple services and applications.”

Moving on from the questions, here is how you should prepare for the Apple data engineer interview:

Understand the Job Description

Apple job descriptions detail every key requirement, responsibility, and sometimes even the pay range. Go through the particulars and understand the specific skillset Apple is seeking for the position. Build a strategy around the requirements and start preparing accordingly.

Master Programming Languages

Proficiency in programming languages, especially Python, is considered a key element in the interview process for Apple’s data engineers. Since Apple prefers multiskilled individuals for their data engineers, consider mastering a few programming languages (e.g., Java, Scala, R, etc.) to gain an edge.

Brush Up on Your Technical Skills

In addition to programming languages, review data querying, data processing, and data warehousing technologies. Be prepared to solve real-life problems related to SQL querying, ETL processes, and big data technologies. Furthermore, familiarize yourself with technologies like Apache Hadoop, Spark, Snowflake, and database management systems.

Also, review the fundamentals of data engineering, as basic topics may also be asked during the interview.

Prepare Technical Interview Questions

Explore our 100+ data engineer interview questions and their solutions to be prepared to ace the technical interview rounds at Apple. Also, feel free to go through our Data Engineer Case Study Interview Guide to efficiently approach the given assignments.

As well, keep practicing data engineering Python questions and SQL problems to equip yourself for the technical rounds.

Prepare Behavioral Questions

Prepare for behavioral questions that assess your problem-solving skills, teamwork, and communication abilities. Be ready to provide examples of how you’ve handled challenges in your previous roles.

Conduct Mock Interviews

Conduct mock interviews with friends, colleagues, or other candidates through our P2P Mock Interview Portal. Moreover, practice explaining your thought process clearly and concisely to our AI-driven Interview Mentor for more transparent feedback on your answers.

What’s the usual salary for data engineers at Apple?

The mean base salary of data engineers at Apple is around $155,000, with the maximum total compensation reaching over $380,000 for senior positions. Read more about the industry-wide salaries in our data engineer salary guide.

As a data engineer, what other companies can I apply for besides Apple?

Data engineers are in demand in most organizations like Apple, including Google, Meta, and Amazon. Read through their job descriptions to find suitable positions according to your skill level and interest. Also, explore other companies through our main company interview guide.

Does IQ post job ads for Apple’s data engineer role?

Yes, we have the latest job posts for Apple’s data engineer role on our job board. But, it’s also good to check the official Apple career page to stay updated on the latest developments.

Apple believes in innovation, privacy, and respect. It promotes safe and supportive workplaces. True to that, Apple actively considers its candidates’ values, cultural alignment, and technical prowess. Challenging you with behavioral and scenario-based interview questions is their way of ensuring compatibility.

Prepare for the Apple data engineer interview questions by refining your programming proficiency, sharpening skills related to data engineering tools, and staying updated on the latest technological advances in the data engineering domain. Practice transparently communicating your project experiences to gain an advantage at the Apple data engineer interview. Our Data Engineering Preparation Guide could be of substantial help in preparing for the position.

If you’re still deciding which role to apply for, check out our other interview guides for business analysts, data analysts, data scientists, and machine learning engineers, as well as the main Apple interview Guide. Interview Query wishes you all the best for your upcoming interview!