Tiger Analytics Data Engineer Interview Questions + Guide in 2024

Companies specializing in Artificial Intelligence (AI) consultation, like Tiger Analytics, are essential in today’s world as they provide expertise and resources needed for other businesses to utilize the advancement of AI technologies.

Consequently, as you can imagine, Tiger Analytics deals with diverse sets of client data and it needs someone with the expertise to manage, store, and process each of the data. This demand has resulted in a high need for Data Engineers.

Although the position is in high demand, the competition to get a Data Engineer position at Tiger Analytics is fierce and you need to be fully prepared during the interview process.

This article aims to guide you through essential aspects of interview preparation for a Data Engineer position at Tiger Analytics. Specifically, we’ll go through the typical interview process of an applicant, examples of typical interview questions, and tips on how to prepare for your interview. So, let’s get started!

To become a Data Engineer at Tiger Analytics, you normally need to pass four different rounds of the interview process. The order of the second and third interview processes below might vary between the company’s locations, but in general, the order of the interview process would be as follows:

1. Application’s Screening

The first step is a general process that you’ll find in any company. After sending your application documents (i.e., resume, cover letter, certificates, etc.), the recruiters at Tiger Analytics will check them and see whether your skills and experiences tick the boxes with regard to the Data Engineer job requirements that they currently need. If yes, then you’ll proceed to the next round.

2. First Technical Round

After you pass the screening round, they will invite you to the first technical round. This technical round will test your knowledge and expertise related to topics like SQL, databases, and file formats. Additionally, you might be asked to write Python code or SQL queries to solve a problem. Thus, it’s necessary for you to brush up on your skills and knowledge related to common tools or platforms (Spark, Kafka, Airflow, Databricks) used in data engineering, especially if those tools are mentioned in the job description.

3. Second Technical Round

At Tiger Analytics, there is a second technical round for a Data Engineer position. However, in this round, the questions are more focused on the technical concept and your past project experience. They might ask you about your experience as a Data Engineer in your previous company, what kind of technology stacks you’ve used and how you implemented them, and some scenario-based questions related to problems that you can solve with common data engineering platforms.

4. Interview with HR

The last interview round normally takes place with one of the HR members. This round talks more about your personality, motivations, and career goals. Also, they will assess whether your value aligns with Tiger Analytics’ culture.

There are various types of questions will be asked during your interview at Tiger Analytics as a Data Engineer applicant, from behavioral questions to technical questions. As part of behavioral questions, you might be asked about career goals you want to achieve and how you envision Tiger Analytics as the right company to achieve those goals, your past experience leading a team, how you dealt with failures, etc.

Meanwhile, the technical questions aim to measure your knowledge related to data engineering, and the questions will typically relate to technologies and platforms that they specify in job descriptions. If the job description mentioned Spark as one of the technology stacks, then you can expect a question or two about Spark.

For a more in-depth discussion, let’s take a look at the examples of interview questions that you might get for a Data Engineer role at Tiger Analytics.

1. Describe a situation where you had to collaborate with cross-functional teams to implement a complex data solution. What were the challenges, and how did you ensure effective communication and alignment of goals across teams?

At a big company like Tiger Analytics, you’ll be collaborating with colleagues across different divisions to ensure the success of the project. This question will assess your collaboration skills and experience working with cross-functional teams.

How to Answer

Start by clearly outlining the context of the collaborative project involving cross-functional teams. Next, mention challenges you’ve faced and describe the strategies implemented that emphasize collaborative efforts.

Example

“In my previous role, I was involved in a complex data integration project that required collaboration between data engineering, data science, and business intelligence teams. One big challenge was the diversity of expertise and priorities among these cross-functional teams.

To address this, me and my colleagues organized regular cross-functional meetings to establish a shared understanding of project goals and timelines. I also made clear documentation of data requirements, schema, and project milestones to mitigate potential misunderstandings across different teams.”

2. Describe the time when your colleagues didn’t agree with your approach. What did you do to solve this, and how did you address their concerns?

Working in a big company like Tiger Analytics requires you to work with colleagues with different personality traits. Also, as a Data Engineer, you will often work with cross-functional teams, and differences in opinions or approaches may arise. This question aims to evaluate how well you handle disagreement, resolve conflicts, and communicate effectively within a team.

How to Answer

First, start by mentioning that diverse perspectives and approaches are common in collaborative settings. Then, demonstrate your flexibility and willingness to consider alternative viewpoints, which then positively impact the project.

Example

“In my previous company, I was involved in a situation where there was a disagreement among colleagues regarding the choice of data storage for a new analytics pipeline. While I recommended a NoSQL solution due to its scalability, some team members preferred a traditional relational database for its familiarity and ease of integration with existing tools.

To address this, I organized a team meeting to openly discuss each approach’s advantages and potential drawbacks. I actively listened to my colleagues’ concerns and reasons for their preferences. Through some discussions, we identified common ground.

In the end, we decided to implement a hybrid solution that utilized a NoSQL database for the analytics pipeline while maintaining a relational database for specific components. This solution allowed us to leverage the benefits of both approaches and ensured the success of the project.”

3. Can you share an example of a time when you had to optimize a data processing workflow to improve performance or efficiency? What challenges did you face, and how did you go about addressing them?

This question will assess your practical experience in optimizing data processing workflows in a large-scale system, which is one of the critical tasks of a Data Engineer at Tiger Analytics.

How to Answer

You can start by briefly describing the context of the optimization challenge, such as by introducing the factors affecting the performance and the challenges that you had in the process of optimizing the workflows. Then, mention the approaches you used to address the challenges and talk about their impact at the end.

Example

“In my previous company, we encountered performance issues in our data processing workflow due to a surge in data volume. The challenges included prolonged processing times and resource inefficiencies.

To address this, I focused on parallelization and data partitioning. I restructured our ETL pipeline to leverage parallel processing, optimized Spark jobs, and introduced strategic data partitioning to distribute the workload evenly.

The result was a significant reduction in data processing times, and data availability demands can then be met.”

4. How do you prioritize deadlines, and how do you stay organized when you have multiple deadlines?

This behavioral question is frequently asked mainly to assess your time management skills. As a Data Engineer at Tiger Analytics, you will often deal with multiple projects and tasks simultaneously. Your ability to effectively prioritize tasks and stay organized for successful completion of projects is an important skill to have.

How to Answer

First, start by mentioning that setting priorities is important in managing multiple projects. Next, highlight your strategies to prioritize tasks and how you manage to stay organized. Make sure that you also emphasize that you’re a highly adaptable person.

Example

“In my previous role as a Data Engineer, I’ve dealt with many multiple projects that require my attention simultaneously. To manage this situation, I always assessed the urgency and importance of each task using tools like a task management system and calendar.

Time blocking is another method that I find effective. With this method, I was able to dedicate specific time slots to different projects based on their priority. Also, I’m always open to regularly reassessing priorities which allows me to adapt to the evolving urgency of a project.”

5. Describe a situation where you had to troubleshoot and resolve a critical issue in a data pipeline. What steps did you take to identify the root cause, and how did you ensure a swift resolution to minimize impact?

This question aims to assess your problem-solving skills and your ability to identify the root cause of unexpected issues, which is a common thing to encounter in your day-to-day life as a Data Engineer.

How to Answer

First, provide the context about the critical issue that you’re trying to solve. Next, mention the steps taken to identify the root cause of the issue and the approach to solve it by highlighting collaborative efforts between you and your colleagues.

Example

“In my previous role, there was a time when our data pipeline encountered a critical issue that was causing delays in data processing, affecting downstream analytics. The impact was substantial, as it could potentially disrupt timely reporting and decision-making.

To address this, I initiated a thorough investigation. I started by reviewing error logs, monitoring metrics, and examining recent changes to the pipeline code. During that stage, I worked closely with the development and operations teams to gather insights.

Identifying a specific issue related to current data schema changes, we then quickly developed a rollback plan to revert to the previous schema while ensuring minimal disruption.”

6. How can you find suitable wine based on certain customer’s conditions?

This question assesses your basic SQL skills and your ability to utilize different types of SQL commands to get the data that you’re looking for. As a Data Engineer at Tiger Analytics, you’re expected to perform data filtering tasks on a daily basis.

How to Answer

First, you need to understand the data that you’ll be working with by reading the columns and the data types of each column. Next, understand the conditions in which you should filter the data. Finally, write your SQL query in a concise manner.

Example

“Since we’re looking for the ID of the customer from a table called wines, then we need to write SELECT id FROM wines. Next, there are several conditions from the customer, in which we need to use a WHERE command. The conditions include: the wine should have greater or equal to than 13% alcohol content, the ash content should be less than 2.4, and the color intensity should be less than 3. Hence the final query would be:

SELECT id FROM wines

WHERE alcohol >= 13

AND ash < 2.4

AND color_intensity < 3

7. Let’s say we have a big immutable dataset with a fixed schema, and we need to perform a selective query on it continuously. Which data format between Parquet and Delta would you use to store the data for this use case? Explain the reason.

As a Data Engineer, the ability to choose the right file format for efficient data storage, processing, and analysis is important, especially in a company like Tiger Analytics, where data scaling is a very important topic as they’re dealing with lots of customer data.

How to Answer

The first thing that you should do is explain what Parquet and Delta are. Next, mention in which scenario they would be best suited to use. Then, based on your explanation, give a recommendation on which one is more suited to use given the scenario in the question.

Example

“Parquet is an open-source columnar file storage format that is highly optimized for query performance due to its ability to skip reading the entire blocks/rows and has built-in data validation for schema enforcement.

Meanwhile, Delta is a type of file storage that will store the modification of a file rather than the whole file itself and, thus, is highly recommended for data where the schema is changing frequently. However, its performance to perform a selective query in general is slower than Parquet’s.

If we have a big immutable dataset with fixed schema and we want to perform a selective query on it frequently, then I would recommend using Parquet instead of Delta.”

8. Given employee data, how can you get the ID of the employee with the largest salary in each department?

This question will assess your basic SQL expertise to perform data processing and queries in an efficient manner. Having a good knowledge of SQL is very handy for a Data Engineer.

How to Answer

First, you need to check the available columns in the dataset as well as the data type of each column. Next, understand the question and the type of data processing that you need to perform. Finally, write the SQL query in a concise manner.

Example

“Given that our employee data has three different columns: id, department, and salary, and we want to fetch the id and the largest salary from a table called employee, then we should write SELECT id, max(salary) as largest_salary FROM employee command.

Next, since we want to obtain the largest salary in each department, then we need to add a GROUP BY department command after the previous SELECT command. Below is the complete SQL command to perform the query:

SELECT

department,

MAX(salary) AS largest_salary

FROM employees

GROUP BY department;

9. Why do you need to cache a dataframe? And how do you clear the cache once it’s stored?

Knowing the technique to cache a dataframe as well as the appropriate time to use it is an essential skill as a Data Engineer. This is especially true when we have lots of dataframe to work with, which is quite typical in a big company like Tiger Analytics.

How to Answer

Start by briefly explaining the purpose of caching in general and why it’s relevant for a DataFrame. Next, start explaining the importance of clearing the cache, especially in a dynamic or memory-constrained environment. Finally, mention the common technique used to clear the cache in a particular library that you’re familiar with (Pandas or Spark).

Example

“Caching is an important strategy to implement when dealing with a large and complex dataset because it allows us to store intermediate results in the memory. This, in turn, will reduce the amount of redundant computations and speed up the querying process.

One important thing to remember when implementing caching is that we also need to clear it once we don’t need the data anymore to free up some memory spaces and avoid some memory-related issues. To clear the cache with Spark, for example, we can use the DataFrame.unpersist() method.”

10. Given an array of unique integers ranging from 0 to n with one value missing, how can you use Python to find the missing number?

Data Engineers at Tiger Analytics frequently work with large datasets and thus, understanding efficient data structures and algorithms becomes a necessity. A Data Engineer with the ability to implement algorithms with the simplest time complexity is highly desirable.

How to Answer

Start by carefully reading the instructions and understand the nature of the problem that you’re trying to solve. Then, try to implement the solution by utilizing your logical and mathematical thinking. As an example, this problem can be solved by calculating the sum of the first n natural number.

Example

“By utilizing the sum of the first n natural number, we can implement an algorithm to solve this problem with the time complexity of O(n), which is very efficient given the problem.

The missing number then can be expressed as the difference between the sum of the first n natural numbers and the actual sum of the given array.”

Sure, here’s the code properly formatted in Markdown:

def find_missing_number(nums):
    # Calculate the expected sum
    expected_sum = (len(nums) + 1) * (len(nums)) // 2

    # Calculate the actual sum of the given array
    actual_sum = sum(nums)

    # Difference between the expected and actual sums
    missing_number = expected_sum - actual_sum

    return missing_number

11. Can you explain what anti left join in PySpark means?

As a Data Engineer, you need to understand different data manipulation techniques, especially if you’re working with large data. Knowing the difference between several types of joins for efficient combination and transformation of the data is essential. As PySpark is one of the technology stacks used by Tiger Analytics, you should demonstrate your skills in using this tool.

How to Answer

Start by briefly explaining the basic description of left join and then introduce the concept of anti left join afterward. Next, explain the purpose of anti join in PySpark and when we should use it.

Example

“In general, a left join combines two dataframes based on a common key, and it will retain all of the records in the left dataframe and the matching records from the right dataframe. Anti left join differs from left join in the sense that this join will retain all of the records in the left dataframe that don’t have a matching key in the right dataframe.

We usually use anti left join in PySpark when we want to filter records in a dataset that don’t have a match in another dataset.”

12. How would you design a schema to represent client click data on the web that will be suitable for various analytics tasks?

As a Data Engineer, you’ll be the frontman of a data pipeline process. The fact that Tiger Analytics gets new data sources on a frequent basis means your ability to architect a flexible schema for a new data source will be important.

How to Answer

First, you need to mention the importance of a versatile schema that supports various analytics tasks. Then, discuss key attributes to include in the schema, emphasizing flexibility and depth of information.

Example

“When setting up analytics tracking for a web app, designing an effective schema for client click data is crucial for capturing and analyzing user interactions. Below is the simple version of my suggested schema:

user_id: A unique id for each user, allowing tracking of individual user interactions.

session_id: A unique id for the user’s session, helping to group multiple clicks during a single user session.

timestamp: The timestamp of the click event, perfect to enable time-based analysis.

page_url: The URL of the web page where the click occurred, providing context about the location of the interaction.

element_id: The id of the clicked element (e.g., button ID, link ID), allowing detailed tracking of user interactions.

element_type: The type of the clicked element (e.g., button, link, image), providing information on the nature of the interaction.”

13. What do you know about NoSQL databases and how they differ from SQL databases?

This question assesses your knowledge of different types of database technologies and your ability to choose appropriate databases given a particular use case. As you might imagine, Tiger Analytics has lots of data sources, each differing from another in terms of their modalities. Understanding the key difference between NoSQL and SQL databases and when to use them to store different types of data is important for you as a Data Engineer.

How to Answer

First, you need to start by defining the description of SQL and NoSQL databases. Next, highlight the main characteristic differences between them. Finally, mention when we should use one rather than another.

Example

“SQL database is a database based on a structured, tabular format, as well as a predefined schema that defines data types and the relationship between tables. Meanwhile, the NoSQL database is a schema-less database, which means that it allows flexibility in terms of its schema.

The key differences between SQL databases and NoSQL databases are as follows:

• SQL databases use a rigid, tabular structure with predefined schemas, while NoSQL databases allow for more flexible, schema-less data structures.
• SQL databases prioritize consistency with ACID properties. This is to ensure that transactions are reliable and predictable. NoSQL databases might prioritize availability and partition tolerance over strict consistency.
• SQL databases typically scale vertically by adding more power to a single server, while NoSQL databases are designed for horizontal scalability, distributing data across multiple servers.

Normally, we want to use SQL databases for applications with complex relationships and transactions, such as financial systems. Meanwhile, we should use NoSQL databases in a use case where we have large volumes of unstructured or semi-structured data, like in web applications, real-time big data processing, or content management systems.”

14. How would you insert an additional column to a dataset consisting of a billion rows without affecting user experience?

Big companies like Tiger Analytics deal with lots of customer data, and thus, schema changes in one of the data are almost inevitable. Thus, they need a Data Engineer with expertise in database management and optimization who is able to implement strategic changes in a production environment.

How to Answer

First, start by mentioning the importance of minimizing the impact of changes on user experience. Then, propose a strategic approach involving common approaches such as creating a new table, bulk insertion, and a seamless transition mechanism.

Example

“I’m aware that as a Data Engineer, optimizing data operations is fundamental. To add a column to a dataset of a billion rows without affecting user experience, I’d follow a two-step strategy: First, create a new table mirroring the existing one with the additional column. By using efficient bulk insertion, I can populate the new column easily. Then, I will implement a mechanism like table renaming or switching for a quick, seamless transition. I would also consider a phased rollout where users gradually switch to the updated table to minimize the impact on the user experience.”

15. What is the difference between repartition and coalesce in Spark?

In a Data Engineer role, the ability to deal with large datasets efficiently is something you need to have. Thus, understanding the concept behind coalescing and repartitioning to manage partitions of data in distributed computing frameworks is becoming necessary. As Spark is the go-to framework at Tiger Analytics, brushing up your Spark knowledge is necessary.

How to Answer

First, briefly explain the importance of coalesce() and repartition(). Then, explain how they differ from each other.

Example

“In the context of distributed data processing frameworks like Apache Spark, repartitioning and coalescing are crucial concepts for optimizing performance and resource utilization. Repartition is used to increase or decrease the RDD, DataFrame, or Dataset partitions whereas the coalesce is used to only decrease the number of partitions in an efficient way.”

16. How would you run data processing and data cleaning of a CSV file that is too big to read with the standard pandas.read_csv()?

At Tiger Analytics, you will mostly be working with large datasets that just don’t fit in the memory. This question assesses your ability to do data processing and data cleaning when the data is just too large and you can’t load them as a whole.

How to Answer

Start by acknowledging that this scenario can happen due to memory constraints and the need to use alternative approaches. Then, suggest practical strategies like reading in chunks, using parallel processing with Dask, employing SQL databases, streaming processing, etc.

Example

“In scenarios where a CSV file surpasses the memory capacity for standard pandas.read_csv(), then we need to use alternative strategies. The easiest strategy that I would suggest is to read the file in manageable chunks using Pandas.read_csv() with the chunksize parameter.

Another option is to leverage Dask for parallel processing, enabling computation on larger-than-memory datasets. Additionally, we can also import the CSV into a SQL database for optimized querying or use streaming processing for line-by-line operations.”

17. How does the RANK() function handle NULL values in the ordered column?

SQL plays a big part in your daily job as a Data Engineer, as you’re going to use it a lot. This question will assess your understanding of different types of SQL commands. Moreover, you need to be aware of the behavior of these SQL commands to ensure accurate and consistent data processing, especially when working with large datasets and analytical tasks.

How to Answer

First, mention the specific behavior of the RANK() function regarding NULL values. Then, explain how RANK() assigns the same rank to rows with NULL values, treating them as equal for ranking purposes.

Example

“In the context of the RANK() function, NULL values in the ordered column are treated as equal, and the function will assign the same rank to rows with NULL values. This behavior ensures that NULL values won’t disrupt the ranking sequence, and the next distinct value receives the subsequent rank.

As an example, if we have a dataset with salaries and some NULL values, using RANK() would result in rows with NULL values sharing the same rank, and the next non-NULL value getting the next rank in sequence.”

18. How do you know if an SQL query takes too long to run?

This is also a question that will test your SQL knowledge. A Data Engineer needs to know when and how to diagnose and address performance issues in SQL queries. As you might be dealing with lots of data ingestion at Tiger Analytics, your ability to spot anomalous query behavior and take appropriate action is important.

How to Answer

Start by mentioning different key indicators of a query taking too long, such as execution time or resource utilization. Then, mention appropriate actions to mitigate the problem, like query profiling and setting timeouts.

Example

“From my experience, we should look at several indicators when SQL queries take too long to run. Normally, I would monitor the execution time, analyze resource utilization, and examine the query execution plan for potential bottlenecks using query profiling tools.”

19. What is Adaptive Query Execution in Spark?

Spark is one of the primary processing systems utilized by Tiger Analytics, and this question will test your knowledge about advanced optimization techniques in Spark, particularly Adaptive Query Execution (AQE). In this question, Tiger Analytics wants to know your awareness of Spark’s ability to dynamically adapt query execution plans based on runtime statistics, which is a crucial aspect of performance tuning in distributed data processing.

How to Answer

First, you can start by explaining the fact that AQE is a special feature in Spark that enables the dynamic adjustment of query execution plans during runtime based on observed statistics. Then, mention its importance in optimizing Spark jobs.

Example

“From my understanding, AQE is a feature that holds significance in optimizing query execution plans dynamically. It allows Spark to adapt to the runtime characteristics of the data being processed. As an example, during the execution of a shuffle stage, AQE can dynamically adjust the number of reducers and partitioning strategies based on observed data sizes. This adaptability enhances the efficiency of Spark jobs by making real-time decisions.”

20. How would you sample a random row in a huge dataset without throttling the database?

As a Data Engineer at Tiger Analytics, you’ll perform SQL queries on a huge dataset frequently. It is important to be mindful when we’re executing the queries, as this action can occasionally result in requests that exceed the rate limit of the company’s database. In other words, you need to know how to implement effective methods when performing SQL queries on large data.

How to Answer

Start by acknowledging the challenge of sampling from a large dataset without impacting database performance. Then, suggest a common SQL command to solve this problem, like TABLESAMPLE.

Example

“In my previous role as a Data Engineer, I was always careful when I needed to sample a random row from a huge dataset. One efficient method I always implemented to avoid database throttling is by using TABLESAMPLE for a specified percentage of the dataset, as it minimizes the impact on performance”

Preparing for your Tiger Analytics Data Engineer interview can be daunting, as you will need to pass several rounds of the interview process, as shown in the previous sections. However, you can increase your chances of success with the right preparations, as shown in the following tips.

Understand the Company and the Role

The very first thing that you should do to prepare for an interview in any company is to research their business. In this case, visit Tiger Analytics’s website and understand their business model, their latest project, the clients they’re working with, etc.

Also, you need to read the job description carefully to understand the kind of task that you’ll be doing there and the knowledge that they expect you to have during the interview process.

Based on that, you can then check out Interview Query’s Data Engineering Learning Path to start brushing up your knowledge. As an example, if they expect you to know about a specific data structure and algorithm concept, then you can practice that topic in the learning path.

Review Fundamentals and Problem-Solving Practice

Brush up on fundamental concepts in data engineering such as databases, SQL, Python, data structures, data modeling, ETL (Extract, Transform, Load) processes, and data warehousing. Also, familiarize yourself with common big data technologies like Spark and Hadoop if any of them are mentioned in the job description.

After you learn the fundamental concepts, then you can start practicing your problem-solving skills by answering different kinds of interview questions. You can find many interview questions tailored towards the Data Engineer position on Interview Query.

Do Mock Interviews

Conducting mock interviews with your friend or mentor is one of the best ways to prepare yourself for an interview, as this method is quite effective in simulating the real interview environment. With a mock interview, you will learn how to articulate your thoughts, and afterward, you’ll also receive constructive feedback from your partner.

If you want to have a mock interview but don’t have a partner yet,  you can sign up for a mock interview on Interview Query and get paired with other aspiring data professionals.

Structure Your Answer when Answering Questions and Ask Questions

It’s completely fine if you don’t know the answer to a technical question during the interview. The more important thing is that you communicate your thought process or hypothesis by walking the interviewer through how you would approach solving the problem asked, even if you don’t know the exact answer.

Meanwhile, during behavioral questions, it’s always better to structure your answer according to the STAR method (Situation, Task, Action, Result). Last, don’t forget to prepare thoughtful questions to ask the interviewers at the end of the interview sessions. This will show your genuine interest in the company and the role.

The coaching service on Interview Query can help you to get better at this. There, you can get guidance and tips from professionals to help you prepare for your interview.

The tips mentioned above are just some high-level tips. If you need more highly detailed tips, then don’t worry, as we have prepared the full guide about this on our site that will help you to prepare for your Data Engineer interview.

These are some of the frequently asked questions by people interested in working as a Data Engineer at Tiger Analytics.

How much do Data Engineers at Tiger Analytics make in a year?

The base salary for Data Engineers at Tiger Analytics ranges between $93,000 and $141,000.

For more insights into the salary range of Data Engineers at various companies, check out our comprehensive Data Engineer Salary Guide.

Where can I read more about other people’s interview experiences for Tiger Analytics’ Data Engineer position on Interview Query?

Interview Query does not have a section on interview experiences for Data Engineer roles at Tiger Analytics. However, you can read about other people’s interview experiences at other companies for a Data Engineer position in our interview experiences section.

You can also interact with other aspiring and working Data Engineers in the IQ community on Slack.

Does Interview Query have job postings for Tiger Analytics’ Data Engineer position?

No, Interview Query does not directly list job postings for Data Engineers position at Tiger Analytics. For job postings, you might refer to their official career page or job boards that specifically list Data Engineer positions.

You can also consider finding new opportunities for a Data Engineer position on Interview Query’s jobs board.

If you need more info about the overview of the interview process for a Data Engineer position at Tiger Analytics, then you can check out our main Tiger Analytics Data Engineer Interview Guide. There, you can go through everything that you need to know about Data Engineer positions, from the range of salaries and interview process to interview questions and discussions.

We’ve also covered their Data Analyst, Data Scientist, and Machine Learning Engineer positions as well, so consider going through those guides if you’re interested.

Preparing for a Data Engineer interview is challenging, as you need to brush up on both your hard and soft skills. However, with the right preparation, you can significantly enhance your chances of getting hired.

Here at Interview Query, our goal is to fully equip you for your Data Engineer interview. To develop your soft skills, we provide a guide on handling behavioral questions. Meanwhile, to enhance your hard skills, we have comprehensive guides on Python, SQL, and case studies. Make sure to check those guides for thorough preparation.

We hope that this guide, as well as other resources available on our platform, will help you to prepare for your Data Engineer interview at Tiger Analytics. Don’t hesitate to contact us if you have any questions or need help. Also, be sure to check our services, as they’re tailored specifically for your needs.