Whether you’re just getting into the data engineer job market or your interview is tomorrow, practice is an essential part of the interview preparation process for a data engineer.
Data engineering interview questions assess your data engineering skills and domain expertise. They are based on a company’s tech stack and technology goals, and they test your ability to perform job functions.
We have detailed the most common skills tested after analyzing 1000+ data engineering interview questions.
To help, we’ve counted down the top 100 data engineering interview questions. These questions are from real-life interview experiences, and they cover essential skills for data engineers, including:
Behavioral questions assess soft skills (e.g., communication, leadership, adaptability), your skill level, and how you fit into the company’s data engineering team.
Behavioral questions are expected early in the data engineering process (e.g., recruiter call) and include questions about your experience.
Examples of behavioral interview questions for a data engineer role would be:
Questions like this assess many soft skills, including your ability to communicate and how you respond to adversity. Your answer should convey:
Your response might demonstrate your experience level, that you take the initiative and your problem-solving approach. This question is your chance to show the unique skills and creative solutions you bring to the table.
Don’t have this type of experience? You can relate your experiences to coursework or projects. Or you can talk hypothetically about your knowledge of data governance and how you would apply that in the role.
Candidates should have an understanding of how data engineering plays into product development. Interviewers want to know how well you’ll fit in with the team, your organizational ability in product development, or how you might simplify an existing workflow.
One Tip: Get to know the company’s products and business model before the interview. Knowing this will help you relate your most relevant skills and experiences. Plus, it shows you did your homework and care about the position.
The STAR framework is the perfect model for answering a question like this. That will cover the how and why. However, one difference with this type of question is showing the value add to your work. For data engineering positions, you might have gone above and beyond, and as a result, you were able to reduce costs, save time, or improve your team’s analytics capacity.
Questions like this assess your communication skills. In particular, interviewers want to know if you can provide clear layperson descriptions of the technology and techniques in data engineering.
For example, you could say: “In a previous job, I was working on a data engineering project. For our developing credit-risk analysis tool, I needed to explain the differences between predictive models (using random forest, KNN, and decision trees). My approach was to distill the definition into easily understandable 1-2 sentence descriptions for each algorithm. Then, I created a short presentation with slides to walk the team through the pros and cons of all three algorithms.”
These questions are common and easy to fail if you haven’t thought through an answer. One option is to focus on the company culture and describe how that excites you about the position.
For example, “I’m interested in working at Google because of the company’s experimentation and engineering innovation history. I look forward to being presented with engineering problems requiring creative, outside-the-box solutions, and I also enjoy developing new tools to solve complex problems. I believe this role would challenge me and provide opportunities to develop novel approaches, which excites me about the role.”
One helpful tip for a question like this: Use an example to illustrate your communication style.
For example, you could say:“I would describe my communication style as assertive. I believe it’s essential to be direct in my project needs and not be afraid to ask questions and gather information.
In my previous position, I was the lead on an engineering project. Before we started, I met with all stakeholders and learned about their needs and wants. One issue that arose was timing, and I felt I would need more resources to keep the project on schedule, so I communicated this to the PM, and we were able to expand the engineering team to meet the tight deadline.”
When interviewers ask this question, they want to see that you can negotiate effectively with your coworkers. Like most behavioral questions, use the STAR method. State the business situation and the task you need to complete. State the objections your coworker had to your action. Do not try to downplay the complaints or write them off as “stupid”; you will appear arrogant and inflexible.
Hint: The most crucial part of your answer is how you resolved the dispute.
This scenario is a variation of the failure question. With this question, a framework like STAR can help you describe the situation, the task, your actions, and the results. Remember: Your answer should provide clear insights into your resilience.
This question assesses your time management skills. Provide specific details on how you operate. You might say I approach projects by:
STAR is a great way to structure your answers to questions like these. You could say:
“My previous job was at a swiping-based dating app. We aimed to increase the number of applications submitted (through swiping). I built an elastic search model to help users see relevant jobs. The model would weigh previous employment information and then use a weighted flexible query on all the jobs within a 50-mile radius of the applicant. After A/B testing, we saw a 10-percent lift in applications, compared to the baseline model.”
This question addresses communication, but it also assesses cultural fit. The interviewer wants to know if you can collaborate and how you present your ideas to colleagues. Use an example in your response:
“In a previous role, I felt the baseline model we were using - a Naive Bayes recommender - wasn’t providing precise enough search results to users. I felt that we could obtain better results with an elastic search model. I presented my idea and an A/B testing strategy to persuade the team to test the idea. After the A/B test, the elastic search model outperformed the Naive Bayes recommender.”
If you have professional experience, choose a project you worked on in a previous job. However, if this is your first job or an internship, you can cite a class or personal project. As you present a data science or data engineering project, be sure to include:
When discussing strengths, ask yourself, “what sets me apart from others?”. Focus on those strengths you can back up with examples using the STAR method, showing how your strength solved a business issue. If you have no prior full-time work experience, feel free to mention takeaways or projects from classes you have taken or initiatives from past part-time jobs.
With weaknesses, interviewers want to know that you can recognize your limits and develop effective strategies to manage the flaws that affect your performance and the business.
Interviewers use easy technical questions designed to weed out candidates without the right experience. This question assesses your experience level, comfort with specific tools, and the depth of your domain expertise. Basic technical questions include:
Data cleaning and data processing are key job responsibilities in engineering roles. Inevitably unexpected issues will come up. Interviewers ask questions like these to determine:
Clearly explain the issue, what you proposed, the steps you took to solve the problem, and the outcome.
There are many variations to this type of question. A different version would be about a specific ETL tool, “Have you had experienced with Apache Spark or Amazon Redshift?” If a tool is in the job description, it might come up in a question like this. One tip: Include any training, how long you’ve used the tech, and specific tasks you can perform.
This question asks: What do you do when there are gaps in your technical expertise? In your response, you might include:
This example is a fundamental case study question in data engineering, and it requires you to provide a high-level design for a database based on criteria. To answer questions like this:
A broad, beginner case study question like this wants to know how you approach a problem. With all case study questions, you should ask clarifying questions like:
These questions will provide insights into the type of response the interviewer seeks. Then, you can describe your design process, starting with choosing data sources and data ingestion strategies, before moving into your developing data processing and implementation plans.
This question assesses how you gather stakeholder information before starting a project. Some of the most common questions to ask would include:
Understanding what stakeholders need from you is essential in any data engineering job, and a question like this assesses your ability to align your work to stakeholder needs. Describe the processes that you typically utilize in your response; you might include tools like:
Ultimately, your answer must convey your ability to understand the user and business needs and how you bring stakeholders in throughout the process.
General experience questions like this are jump-off points for more technical case studies. And typically, The interviewer will tailor questions as they pertain to the role. However, you should be comfortable with standard Python and supplemental libraries like Matplotlib, Pandas, and NumPy, know what’s available, and understand when it’s appropriate to use each library.
One note: Don’t fake it. If you don’t have much experience, be honest. You can also describe a related skill or talk about your comfort level in quickly picking up new Python skills (with an example).
If cloud technology is in the job description, chances are it will show up in the interview. Some of the most common cloud technologies for data engineer interviews include Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM Cloud. Additionally, be prepared to discuss specific tools for each platform, like AWS Glue, EMR, and AWS Athena.
A broad question like this can quickly assess your experience with cloud technologies in data engineering. Some of the challenges you should be prepared to talk about include:
With a fundamental question like this, be prepared to answer with a quick definition and then provide an example.
You could say: “Structured data consists of clearly defined data types and easily searchable information. An example would be customer purchase information stored in a relational database. Unstructured data, on the other hand, does not have a clearly defined format, and therefore, a relational database can’t store it in a relational database. An example would be video or image files.”
Some of the Hadoop features you might talk about in a data engineering interview include:
SQL questions for data engineers cover fundamental concepts like joins, subqueries, case statements, and filters. In addition, if required to write SQL code, it could test if you know how to pull metrics or questions that determine how you handle errors and NULL values. Common SQL questions include:
Both of these commands will delete data. However, a key difference is that DELETE is a Database Manipulation Language (DML) command, while TRUNCATE is a Data Definition Language (DDL) command.
Therefore, DELETE is used to remove specific data from a table, while TRUNCATE removes all table rows without maintaining the table’s structure. Another difference: DELETE can is available with the WHERE clause, but TRUNCATE cannot. In this case, DELETE TABLE would remove all the data from within the table while maintaining the structure, and TRUNCATE TABLE would completely delete the table.
Both WHERE and HAVING are used to filter a table to meet the conditions that you set. The difference between the two is apparent when used in conjunction with the GROUP BY clause. The WHERE clause filters rows before grouping (before the GROUP BY clause), and HAVING is used to filter rows after collection.
Indexes are lookup tables used by the database to perform data retrieval more efficient. Users can use an index to speed up SELECT or WHERE clauses, but they slow down UPDATE and INSERT statements.
An aggregate function performs a calculation on a set of values and returns a single value summarizing the background. SQL’s three most common aggregate functions are COUNT, SUM, and AVG.
COUNT - Returns the number of items of a group. SUM - Returns the sum of ALL or DISTINCT values in an expression. AVG - Returns the average of values in a group (and ignores NULL values)
Some of the most common SQL functions used in the data extraction process include SELECT, JOIN, WHERE, ORDER BY, and GROUP BY.
How you join tables can have a significant effect on query performance. For example, if you JOIN large tables and then JOIN smaller tables, you could increase the processing necessary by the SQL engine. One general rule: joining two tables that will reduce the number of rows processed in subsequent steps will help to improve performance.
You would do this with the RENAME and ALTER TABLE functions. Here’s an example syntax for changing a column name in SQL:
ALTER TABLE TableName RENAME COLUMN OldColumnName TO NewColumnName;
You might want to clarify a question and ask some follow-up questions of your own. Specifically, you might be interested in A. what kind of data is processed, B., and what types of values can users duplicate?
With some clarity, you’ll be able to suggest more relevant strategies. For example, you might propose using a distinct or unique key to reduce duplicate data. Or you could walk the interviewer through how the GROUP BY key.
Hint: Given two date ranges, what determines if the subscriptions overlap? If one field is after the other, nor entirely before the other, then the two ranges must overlap.
To answer this SQL question, you can think of De Morgan’s law, which says that:
Not (A Or B) <=> _Not A And Not B_.
What is the equivalent? And how could we model that out for a SQL query?
Follow-up question. Rank them by the percentage of employees making $100,000+.
This question is an example of a multi-part logic-based SQL question that data engineers face. With this SQL question, you need:
Whenever the question asks about finding “0 values,” e.g., users or neighborhoods, start thinking LEFT JOIN! An inner join finds any values in both tables; a LEFT JOIN keeps only the values in the left table.
With this question, our predicament is to find all the neighborhoods without users. To do this, we must do a left join from the neighborhoods table to the user’s table. Here’s an example solution:
SELECT n.name FROM neighborhoods AS n LEFT JOIN users AS u ON n.id = u.neighborhood_id WHERE u.id IS NULL
This question is used in Facebook data engineer interviews.
More context. You have two tables: projects (with columns id, title, start_date, end_date, budget) and employees_projects (with columns project_id, employee_id). You must select the five most expensive projects by budget to employee count. However, due to a bug, duplicate rows exist in the employees_projects table.
One way to remove duplicates from the employees_projects table would be to GROUP BY the columns project_id and employee_id simply. By grouping by both columns, we’ve created a table that sets distinct values on project_id and employee_id, thereby eliminating duplicates.
More context. Use when given a table of bank transactions with id, transaction_value, and created_at, a DateTime for each transaction.
Start by trying to apply a window function to make partitions. Because the created_at column is a DateTime, multiple entries can be for different times on the same date. For example, transaction 1 could happen at ‘2020-01-01 02:21:47’, and transaction 2 could happen on ‘2020-01-01 14:24:37’. To make partitions, we should remove information about when the transaction was created. But, we still need that information to sort the transactions.
To do this, you could try:
ROW_NUMBER() OVER (PARTITION BY DATE(created_at) ORDER BY created_at DESC
Now, how would you get the last transaction per day?
To answer this question, we need to apply an average function to the quantity for every different year and product_id combination. We can extract the year from the created_at column using the YEAR() function. We can use the ROUND() and AVG() functions to round the average quantity to 2 decimal places.
Finally, we make sure to GROUP BY the year and product_id to get every distinct year and product_id combination.
Be prepared for a wide range of data engineer Python questions. Expect questions about 1) data structures and data manipulation (e.g., Python lists, data types, data munging with pandas), 2) explanations (e.g., tell us about search/merge), and 3) Python coding tests. Sample Python questions include:
This is a simple Python definition that’s important to know. In general, “==” is used to determine if two objects have the same value. And “is” determines if two references refer to the same object.
In Python, a decorator is a function that takes another function as an argument and returns a closure. The closure accepts positional or keyword-only arguments or a combination of both, and it calls the original function using the arguments passed to the closure.
Decorators help add logging, test performance, perform caching, verify permissions, or when you need to run the same code on multiple functions.
With this question, outline the process you use for scraping with Python. You might say:
“First, I’d use the request library to access the URL and extract data using BeautifulSoup. With the raw data, I would convert it into a structure suitable for pandas and then clean the data using pandas and NumPy. Finally, I would save the data in a spreadsheet.”
Dictionaries are faster. One way to think about this question is to consider it through the lens of Big O notation. Dictionaries are faster because they have constant time complexity O(1), but for lists, it’s linear time complexity or O(n). With lists, you have to go through the entire list to find a value, while with a dictionary, you don’t have to go through all keys.
How familiar type questions show up early in the interview process? You might hear these in the technical interview or on the recruiter screen. If a Python tool, skill, or library is mentioned in the job description, you should expect a question like this in the interview.
You could say: “I have extensive experience with TensorFlow. In my last job, I developed sentiment analysis models, which would read user reviews and determine the polarity of the text. I developed a model with Keras and TensorFlow and used TensorFlow to encode sentences into embedding vectors. I built most of my knowledge through professional development courses and hands-on experimentation.”
Both lists and tuples are common data structures that can store one or more objects or values and are also used to store multiple items in one variable. However, the main difference is that lists are mutable, while tuples are immutable.
Data smoothing is a technique that eliminates noise from a dataset, effectively removing or “smoothing” the rough edges caused by outliers. There are many different ways to do this in Python. One option would be to use a library like NumPy to perform a Rolling Average, which is particularly useful for noisy time-series data.
NumPy is one of the most popular Python packages, along with pandas and Matplotlib. NumPy adds data structures, including a multidimensional array, to Python, which is used for scientific computing. One of the benefits of using NumPy arrays is that they’re more compact than Python lists, and therefore, it consumes less memory.
A cache database is a fast storage solution for short-lived structured or unstructured data. Generally, this database is much smaller than a production database and can store in memory.
Caching is helpful for faster data retrieval because Users can access the data from a temporary location. There are many ways to implement caching in Python, and you can create local data structures to build the cache or host a cache as a server, for example.
The built-in data types in Python include lists, tuples, dictionaries, and sets. These data types are already defined and supported by Python and act as containers for grouping data by type. User-defined data types share commonalities with primitive types, and they are based on these concepts. But ultimately, they allow users to create their data structures, including queues, trees, and linked lists.
This question asks you to aggregate lists in Python, and your goal is an output like this:
def weekly_aggregation(ts) -> [ ['2019-01-01', '2019-01-02'], ['2019-01-08'], ['2019-02-01', '2019-02-02'], ['2019-02-05'], ]
Hint: This question sounds like it should be a SQL question. Weekly aggregation implies a form of GROUP BY in a regular SQL or pandas question. But since it’s a scripting question, it’s trying to pry out if the candidate deals with unstructured data. Data scientists deal with a lot of unstructured data.
Given that we have to return the first index of the second repeating character, we should be able to go through the string in one loop, save each unique character, and then check if the character exists in that saved set. If it does, return the character. Here’s a sample output for this question:
input = "interviewquery" output = "i" input = "interv" output = "None"
This type of question is the classic subset sum problem presented in a way that requires us to construct a list of all the answers. Subset sum is a type of problem in computer science that broadly asks to find all subsets of a set of integers that sum to a target amount.
We can solve this question through recursion. Even if you didn’t recognize the problem, Users could guess at its recursive nature if you recognize that the problem decomposes into identical subproblems when solving it. For example, if given integers = [2,3,5] and target = 8 as in the prompt, we might recognize that if we first solve for the input: integers = [2, 3, 5] and target = 8 - 2 = 6, we can just append 2 to each combination in the output to obtain our final answer. This subproblem recursion is the hallmark of dynamic programming and many other related recursive problem types.
Let’s first think of a base case for our recursive function.
Bigrams are two words grouped next to each other, and they’re relevant in feature engineering for NLP models. With this question, we’re looking for output like this:
def find_bigrams(sentence) -> [('have', 'free'), ('free', 'hours'), ('hours', 'and'), ('other', 'activities.')]
Solution overview: To parse them out of a string, you must split the input string. You can do this with the python function .split() to create a list with each word as an input. Then, create another empty list that a user will eventually fill with tuples.
This question has appeared in Google data engineer interviews.
Data modeling and database design questions assess your knowledge of entity-relationship modeling, normalization and denormalization tradeoffs, dimensional modeling, and related concepts. Common questions include:
The physical database model is the last step before implementation and includes a plan for how you will build the database. Based on the requirements of the build, the physical model typically differs from the logical data model.
Some of the key features of the physical data model include:
With this question, explain the types of relationships you know, and provide examples of the work you’ve done with them. The four main types of database relationships include:
Complex migrations can result in data loss, and data engineering candidates should have ideas for minimizing loss. A few steps you can take to reduce data loss during migration would include:
The three most commonly used data models are relational, dimensional, and entity-relationship. However, many others aren’t widely used, including object-oriented, multi-value, and hierarchical. The type of model used defines the logical structure and how it is organized, stored, and retrieved.
Data normalization is organizing and formatting data to appear similar across all records and fields. Data normalization helps provide analysts with more efficient and precise navigation, removing duplicate data and maintaining referential integrity.
On the other hand, Denormalization is a database technique in which redundant data is added to one or more tables. This technique can optimize performance by reducing the need for costly joins.
Some of the most common mistakes when modeling data include:
Compared to relational databases, NoSQL databases have many advantages, including scalability and superior performance. Some of the benefits of NoSQL databases include:
Let’s first approach this problem by understanding the scope of the dating app and what functionality we must design around it.
Start by listing out 1) essential app functions for users (e.g., onboarding, matching, messaging) and 2) specific feature goals to account for (e.g., hard or soft user preferences or how the matching algorithm works).
With this information, we can create an initial design for the database.
In this two-part table schema question, we’re tracking not just enter/exit times but also car make, model, and license plate info.
The car model for licensing plate information will be one-to-many, given that each license plate represents a single car, and each car model can replicate many times. Here’s an example for crossings (left) and model/license plate (right):
This question is more architecture-based and assesses experience within developing databases, setting up architectures, and in this case, representing client-side tracking in the form of clicks.
A simple but effective design schema would be to represent each action with a specific label. In this case, it assigns each click event a name or title describing its particular action.
Many database design questions for data engineers are vague and require follow-up. With a question like this, you might want to ask: What’s the potential impact of downtime?
Don’t rush into answers to questions. A helpful tip for all Python and technical questions is to ask for more information, and this shows you’re thoughtful and look at problems from every angle.
In addition, you’re tasked with using the star schema for your design. The star schema is a database structure that uses one primary fact table to store transactional data and a smaller table or tables that store attributes about the data.
Some key transactional details you would want to include in the model:
– orders - orderid, itemid, customerid, price, date, payment, promotion
– customer - customer_id, cname, address, city, country, phone
– items - itemid, subcategory, category, brand, mrp
– payment - payment, mode, amount
– promotions - promotionid, category, discount, start_date, end_date
– date - datesk, date, month, year, day
Follow-up question: How would the table schema look?
See a complete mock interview solution for this database design question on YouTube:
Data engineering case studies, or “data modeling case studies,” are scenario-based data engineering problems. Many questions focus on designing architecture, and then you walk the interviewer through developing a solution.
A simple four-step process for designing relational databases might include these steps:
How do you go about debugging an ETL error?
Start your response by gathering information about the system. What tools are used, and how does the existing process look?
Next, you could talk about two approaches:
At a minimum, ETL process failure should log details of the loss via a logging subsystem. Log data is one of the first places you should look to triage an error.
With architecture problems, you should have a firm grasp of design patterns, technologies, and products users can use to solve the problem. You might talk about some of the most common database patterns you’ve used like:
Many case study questions for data engineers are similar to database design questions. With a question like this, start with clarifying questions. You might want to know the goals for the notification system, user information, and the types of notifications utilized.
Then, you’ll want to make assumptions. A primary solution might start with notifications:
Follow-up question. How would you debug what happened? What data would you look into, and how would you determine who is married and who is not?
With this debugging data question, you should start with some clarification, e.g., how far back does the bug extend? What’s the table schema appearance? One potential solution would be to look at other dimensions and columns that might be able to answer if someone is married (like marriage data or spouse’s name).
Amazon data engineer interviews have utilized this question
When answering this question, you might want to start with questions about the goals and uses of the database. You want to design a database for how the company will use the data.
The biggest beneficiary of optimizations would likely be increasing the speed and performance of the locations and swipes table. While we can easily add an index to the locations table on something like zip code (U.S.-only assumptions), we can’t add one to the swipes table, given the size of that table. One thing to consider when adding indices is that they trade off space for access speed.
One option is to implement a sharded design for our database. While indexing does a table copy and rearranges records to allow you to read off of a table sequentially, sharding will enable you to add multiple nodes where the specific record you want is only on one of those nodes. This process allows for a more bounded result in terms of retrieval time.
What other optimizations might you consider?
This question requires clarity: What exactly is a wrong or missing order? For example, if the wrong order means “orders that users placed but ultimately canceled,” you’d have a binary classification problem.
If, instead, it meant “orders in which customers provided a wrong address or other information,” you might try to create a classification model to identify and prevent wrong information from being added.
The purpose of a recommendation algorithm is to recommend videos that a user might like. One way to approach this would be to suggest metrics that indicate how well a user likes a video. Let’s say we set a metric to gauge user interest in a video: whether users watch a whole video or stop before the video completes.
Once we have a functioning metric for whether users like or dislike videos, we can associate users with similar interests and attributes to generate a basic framework for a recommendation. Our approach relies on the assumption that if person A likes a lot of the things that person B likes or is similar in other respects (such as age, sex, etc.), there’s an above-average chance that person B will enjoy a video that person A likes.
What other factors might we want to take into account for our algorithm?
Data engineers and data scientists work hand in hand. Data engineers are responsible for developing ETL processes, analytical tools, and storage tools and software. Thus, expertise with existing ETL and BI solutions is a much-needed requirement.
ETL refers to collecting extraction data from a data source, converting (transformation) into a format that users can easily analyze, and storing (loading) into a data warehouse. The ETL process then loads the transformed data into a database or BI platform to be used and viewed by anyone in the organization. The most common ETL interview questions are:
Many problems can arise from concurrent transactions. One is lost updates. Lost updates occur when a committed value written by one transaction overrides a subsequently committed write from a simultaneous transaction. Another is write skew, which happens when updates within a transaction based upon stale data is made.
The critical point to remember for this question is that ETL transforms the data outside the warehouse. In other words, no raw data will transfer to the warehouse. In ELT, the transformation takes place in the warehouse; the raw data goes directly there.
There are two primary ways to load data into a data warehouse: initial and full load. The differences between initial and full load are:
Full loads take more time and include all rows but are less complicated. Initial loads take less time (because they contain only new or updated records ) but are more challenging to implement and debug.
You should be comfortable talking about the tools with which you are most skilled. However, if you do not have experience with a specific tool, you can do some pre-interview preparation.
Start by researching the ETL tools the company already uses. Your goal should be a solid overview of the tools’ most common processes and uses. The most common ETL platforms, frameworks, and related technologies are:
Note: If you only have basic tool knowledge, do not be afraid to admit it. However, describe how you learn new tools and how you can leverage your existing expertise in evaluating the unknown tool.
Partitioning is the process of subdividing data to improve performance. The data is partitioned into smaller units, allowing for more straightforward analysis. You can think of it like this: partitioning will enable you to add organization to a large data warehouse, similar to signs and aisle numbers in a large department store.
This practice can help improve performance, aid in management, or ensure the data stays available (if one partition is unavailable, other partitions can remain open).
In short, a snapshot is like a photo of the database. It captures the data from a specific point in time. Database snapshots are read-only, static views of the source database. Snapshots have many uses, including safeguarding against admin errors (by reverting to the snapshot if an error occurs), reporting (e.g., a quarterly database snapshot), or test database management.
Creating views may be a step in the transformation process. A view is a stored SQL query that an interface can store for use in the database environment. Users build views with a database management tool.
Knowing the limitations and weaknesses of ETL is critical to demonstrate in ETL interviews. It allows you to assess, find workarounds or entirely avoid specific processes that may slow the production of relevant data.
For example, staging and transformation are incredibly time-intensive. Moreover, if the sources are unconventional or inherently different, the transformation process might take a long time. Another bottleneck of ETL is the involvement of hardware, specifically disk-based pipelines, during transformation and staging. The hardware limitations of physical disks can create slowdowns that no efficient algorithm can solve.
The first thing to do when checking for errors is to test whether one can duplicate the error.
An operational data store, or ODS, is a database that provides interim storage for data before it’s sent to a warehouse. AN ODS typically integrates data from multiple sources and provides an area for efficient data processing activities like operational reporting. Because an ODS typically includes real-time data from various sources, they provide up-to-date snapshots of performance and usage for order tracking, monitoring customer activity, or managing logistics.
For this problem, we use the INSERT INTO keywords to add rows to the lifetime_plays table. If we set this query to run daily, it becomes a daily extract, transform, and load (ETL) process.
The rows we add from the subquery that selects the created_at date, user_id, song_id, and count columns, are chosen from the song_plays table for the current date.
With a question like this, a business would provide you with a table representing the company payroll schema.
Hint. The first step we need to do would be to remove duplicates and retain the current salary for each user. Given that there aren’t any duplicate first and last name combinations, we can remove duplicates from the employee’s table by running a GROUP BY on two fields, the first and last name. This process allows us then to get a unique combinational value between the two fields.
Data engineers focus primarily on data modeling and data architecture, but a basic knowledge of algorithms and data structure is also needed. The data engineer’s ability to develop inexpensive methods for transferring large amounts of data is of particular importance. If you’re responsible for a database with potentially millions (let alone billions) of records, finding the most efficient solution is essential. Common algorithm interview questions include:
There are many algorithms and approaches to handling missing values. You might cite these missing value algorithms:
You might also want to incorporate some of the pros and cons of using algorithms for missing values. For example, a downfall is that it tends to be time-consuming.
Linear data structures are elements that attach to the previous and adjacent elements and only involve a single level. In non-linear structures, data elements attach hierarchically, and multiple levels are involved. With linear data structures, elements can only be traversed in a single run, whereas in non-linear structures, they cannot.
Examples of linear data structures include queue, stack, array, and linked list. Non-linear data structures include graphs and trees.
Some potential uses for linked lists include maintaining text directories, implementing stacks and queues, representing sparse matrices, or performing math operations on long integers.
Use list comprehension to print odd numbers between 0 and 100.
List comprehension defines and creates a list based on an existing list.
list1 = [0, 1, 2, 3, 4, 5.. 99,100] only_odd = [num for num in list1 if num % 2 == 1] print(only_odd)
This question asks you to create a queue that supports enqueue and dequeue operations, using the stack’s push and pop operations. A queue is a first-in, first-out structure in which elements are removed in the order in which the process adds them.
One way to do this would be with two stacks.
To enqueue an item, for example, you would move all the elements from the first stack to the second, push the item into the first, and then move all elements back to the first stack.
Dequeue is a queue operation to remove items from the front of a queue.
There are several linear regression assumptions, which are baked into the dataset and how the model is built. Otherwise, if these assumptions are violated, we become privy to the phrase “garbage in, garbage out”.
The first assumption is that there is a linear relationship between the features and the response variable, otherwise known as the value you’re trying to predict. This assumption is baked into the definition of linear regression.
What other assumptions exist?
nums = [0, 1, 2, 4, 5] missingNumber(nums) -> 3
We can solve this problem in a logical iteration or mathematical formulation.
S 3 4 5 6 2 4 6 1 1 9 9 9 9 E
Here’s a solution: Recursive backtrack to the end while saving the max path water level on each function call. Track a visited cell set to trim the search space. O(n^2)
The brute force solution to this question will be to try every permutation and verify if it’s a palindrome. If we find one, then return true; otherwise, return false. You can see the complete solution on Interview Query.
A function that is O(1) means it does not grow with the input data size. For this problem, the function must loop through the stream, inputting two entries at a time and choosing between the two with a random method.
Here’s a solution for this Python data structures question:
import bisect def index(a, x): i = bisect.bisect_left(a, x) return i a = [1,2,4,5] print(index(a, 6)) print(index(a, 3))
The best way to prepare for a data engineer interview is practice. Practice as many example interview questions as possible, focusing primarily on the most important skills for the job, as well as where you have gaps in knowledge.