Sharethrough Data Engineer Interview Questions + Guide in 2025

Sharethrough is a leading platform in the advertising technology industry, specializing in providing innovative solutions for publishers and advertisers to maximize their revenue and engagement through programmatic advertising.

As a Data Engineer at Sharethrough, you will be responsible for designing, building, and maintaining scalable data pipelines that facilitate the collection, storage, and processing of large datasets. Your role will involve collaborating closely with data scientists, product teams, and other stakeholders to ensure that data is accessible, reliable, and actionable. Key responsibilities include developing ETL processes, optimizing database performance, and ensuring data quality through comprehensive testing and validation.

To excel in this role, you should possess strong programming skills in languages such as Scala and Python, as well as a solid understanding of SQL for managing and querying databases. Familiarity with data processing frameworks and tools, such as Apache Spark and Pandas, is highly beneficial. The ideal candidate will be detail-oriented, possess excellent problem-solving abilities, and have a passion for working with data in a fast-paced environment.

This guide will equip you with insights into the expectations and challenges of the Data Engineer role at Sharethrough, helping you prepare effectively for your interview.

The interview process for a Data Engineer at Sharethrough is structured to assess both technical skills and cultural fit within the team. It typically consists of several key stages:

1. Initial HR Interview

The process begins with a 30-minute phone interview with a recruiter or HR representative. This conversation is designed to provide an overview of the role and the company culture. The recruiter will ask about your background, experiences, and motivations for applying, while also gauging your alignment with Sharethrough's values.

2. Technical Interview

Following the initial HR interview, candidates will participate in a one-hour technical interview. This session often involves a pair programming exercise where you will work collaboratively with the interviewer to solve a data processing problem. Expect to demonstrate your proficiency in relevant programming languages, such as Scala or Python, and your ability to utilize data manipulation libraries like Pandas. The focus will be on your problem-solving approach and coding skills rather than just the final solution.

3. Group Interview

The final stage of the interview process is a group interview, which typically lasts around 2.5 hours. During this session, you will meet with multiple team members who will assess your technical abilities, teamwork, and communication skills. This format allows the team to evaluate how you interact with others and how well you can articulate your thought process while working on data engineering challenges. The atmosphere is generally friendly, encouraging open dialogue and collaboration.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during these stages.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Sharethrough. The interview process will likely assess your technical skills in data processing, programming, and your ability to work collaboratively within a team. Be prepared to demonstrate your knowledge of data engineering concepts, as well as your proficiency in relevant programming languages and tools.

Technical Skills

1. Can you explain the differences between SQL and NoSQL databases?

Understanding the strengths and weaknesses of different database types is crucial for a Data Engineer.

How to Answer

Discuss the characteristics of both SQL and NoSQL databases, including their use cases, scalability, and data structure differences.

Example

“SQL databases are relational and use structured query language for defining and manipulating data, making them ideal for complex queries and transactions. In contrast, NoSQL databases are non-relational and can handle unstructured data, which is beneficial for applications requiring high scalability and flexibility, such as real-time analytics.”

2. Describe a data pipeline you have built. What tools did you use?

This question assesses your practical experience in building data pipelines.

How to Answer

Outline the steps you took to design and implement the pipeline, including the tools and technologies you utilized.

Example

“I built a data pipeline using Apache Airflow to automate the extraction of data from various sources, transform it using Python scripts, and load it into a PostgreSQL database. This pipeline improved data availability for our analytics team and reduced processing time by 30%.”

3. How do you ensure data quality in your projects?

Data quality is critical in data engineering, and interviewers want to know your approach.

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools that assist in maintaining data integrity.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Great Expectations to automate testing. Additionally, I regularly monitor data quality metrics and conduct audits to identify and rectify any discrepancies.”

4. What is your experience with cloud platforms for data engineering?

Familiarity with cloud services is often essential for modern data engineering roles.

How to Answer

Mention specific cloud platforms you have worked with and how you utilized them in your projects.

Example

“I have extensive experience with AWS, particularly using services like S3 for data storage and Redshift for data warehousing. I also leverage AWS Lambda for serverless data processing, which allows for efficient scaling based on demand.”

5. Can you walk us through a coding exercise you completed in Scala?

Since Scala is mentioned, this question tests your programming skills in a relevant language.

How to Answer

Be prepared to discuss a specific coding challenge, including your thought process and the solution you implemented.

Example

“In a recent project, I was tasked with processing large datasets in Scala. I wrote a program that utilized Spark to perform transformations and aggregations efficiently. The challenge was to optimize the performance, so I implemented partitioning strategies that significantly reduced processing time.”

Data Processing and Analytics

1. How do you handle missing or corrupted data in a dataset?

This question evaluates your problem-solving skills in data preprocessing.

How to Answer

Explain your approach to identifying and addressing missing or corrupted data, including any techniques or tools you use.

Example

“I typically start by analyzing the dataset to understand the extent of missing values. Depending on the situation, I may choose to impute missing values using statistical methods or remove records with excessive missing data. For corrupted data, I implement validation rules to catch errors early in the data pipeline.”

2. What are some common data transformation techniques you use?

This question assesses your knowledge of data manipulation.

How to Answer

Discuss various transformation techniques and when you would apply them.

Example

“I frequently use normalization and standardization techniques to prepare data for analysis. Additionally, I apply techniques like one-hot encoding for categorical variables and aggregation functions to summarize data for reporting purposes.”

3. Describe a time when you had to optimize a slow-running query. What steps did you take?

This question tests your analytical skills and understanding of performance tuning.

How to Answer

Outline the steps you took to identify the performance issue and the optimizations you implemented.

Example

“I noticed that a particular SQL query was taking too long to execute. I analyzed the execution plan and found that it was performing full table scans. I optimized the query by adding appropriate indexes and rewriting it to reduce complexity, which improved performance by over 50%.”

4. How do you approach data modeling?

This question evaluates your understanding of data architecture.

How to Answer

Discuss your methodology for designing data models, including any frameworks or best practices you follow.

Example

“I start by gathering requirements from stakeholders to understand the data needs. Then, I create an Entity-Relationship Diagram (ERD) to visualize the relationships between data entities. I follow normalization principles to reduce redundancy while ensuring the model supports efficient querying.”

5. What tools do you use for data visualization and reporting?

This question assesses your experience with data presentation.

How to Answer

Mention specific tools you are familiar with and how you have used them in your work.

Example

“I often use Tableau for data visualization, as it allows for interactive dashboards that can be easily shared with stakeholders. Additionally, I utilize Python libraries like Matplotlib and Seaborn for custom visualizations during data analysis.”