Sojern Data Engineer Interview Questions + Guide in 2025

Sojern is a data-driven travel marketing platform that helps brands reach travelers with precision and efficiency.

As a Data Engineer at Sojern, you will play a pivotal role in developing and maintaining scalable data pipelines that enable the analysis of travel-related data. Your responsibilities will include designing data architectures, optimizing database performance, and ensuring data integrity across various systems. You will collaborate closely with data scientists and analysts to understand their data needs and provide efficient solutions. A strong background in SQL, Python, and data modeling is essential, along with familiarity with cloud technologies and big data tools. Candidates with a proactive mindset and a passion for problem-solving will thrive in this role, as they will be instrumental in driving the company's data initiatives and supporting its commitment to providing superior travel insights.

This guide will help you prepare for a job interview by highlighting the essential skills and experiences sought by Sojern for the Data Engineer role, while also providing insights into the company culture and interview process.

The interview process for a Data Engineer role at Sojern is structured to assess both technical skills and cultural fit within the company. The process typically unfolds as follows:

1. Initial HR Screening

The first step in the interview process is a phone call with a recruiter from the HR team. This conversation usually lasts around 30 minutes and serves as an introduction to the company and the role. During this call, the recruiter will inquire about your background, motivations for applying, and your understanding of the Data Engineer position. It’s also an opportunity for you to ask questions about the company culture and the team dynamics.

2. Technical Screening

Following the initial HR screening, candidates typically undergo a technical screening, which may be conducted via video call. This session often involves a technical manager or a senior data engineer who will assess your technical competencies. Expect to discuss your experience with programming languages such as SQL and Python, as well as your understanding of data architecture and design principles. You may also be asked to solve coding challenges or design-level questions to demonstrate your problem-solving abilities.

3. Onsite Interviews

The onsite interview process generally consists of multiple rounds, often totaling around four to five sessions. Each interview lasts approximately 45 minutes and may include a mix of technical assessments and behavioral questions. Candidates can expect to engage with cross-functional teams, which may include data scientists, product managers, and other engineers. During these interviews, you will likely be asked to present a project you’ve worked on, detailing the business problem, methodology, and insights generated. This is a chance to showcase your critical thinking and technical skills in a collaborative environment.

4. Final Assessment and Feedback

After the onsite interviews, candidates may experience a waiting period for feedback. This stage can vary in length, and it’s not uncommon for candidates to feel uncertain about their status. However, it’s important to remain patient and proactive in following up for updates. The final decision will be based on the collective feedback from all interviewers, and candidates may receive an offer or constructive feedback on their performance.

As you prepare for your interview, consider the types of questions that may arise during this process.

Here are some tips to help you excel in your interview.

Understand the Company Culture

Sojern emphasizes a friendly and welcoming environment, so approach your interview with a positive attitude. Be prepared to engage in conversations that reflect your personality and values. Familiarize yourself with the company's commitment to diversity and inclusion, and be ready to discuss how your background and experiences can contribute to a more inclusive workplace. This understanding will help you connect with your interviewers on a personal level.

Prepare for Technical Assessments

As a Data Engineer, you will likely face multiple technical rounds, including coding challenges and design-level questions. Brush up on your SQL and Python skills, as these are commonly tested. Practice coding problems that are at an easy to moderate level, focusing on data manipulation and engineering concepts. Additionally, be prepared to discuss your previous projects in detail, highlighting the business problems you solved, the methodologies you used, and the insights you generated.

Craft Your Stories

Given the subjective nature of the decision-making process, having well-prepared stories about your past experiences can significantly enhance your chances. Structure your stories using the STAR (Situation, Task, Action, Result) method to clearly articulate your contributions and the impact of your work. Tailor these stories to demonstrate your technical skills, critical thinking, and ability to work collaboratively in cross-functional teams.

Be Ready for Behavioral Questions

Expect to answer behavioral questions that assess your problem-solving abilities and teamwork. Reflect on your past experiences and be ready to discuss challenges you faced, how you prioritized tasks, and your approach to collaboration. This will not only showcase your skills but also align with Sojern's emphasis on a supportive team environment.

Follow Up and Seek Clarity

Given the feedback from candidates about the hiring process, it’s important to maintain open communication. After your interview, send a thank-you email to express your appreciation for the opportunity and reiterate your interest in the role. If you don’t receive feedback within the promised timeframe, don’t hesitate to follow up politely. This demonstrates your enthusiasm and professionalism, which can leave a positive impression.

By keeping these tips in mind, you can navigate the interview process at Sojern with confidence and clarity, positioning yourself as a strong candidate for the Data Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Sojern. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data management and engineering principles. Be prepared to discuss your past projects, methodologies, and the impact of your work on business outcomes.

Technical Skills

1. Can you describe a data pipeline you built and the technologies you used?

This question assesses your hands-on experience with data engineering and the tools you are familiar with.

How to Answer

Discuss the architecture of the pipeline, the technologies used (e.g., ETL tools, databases), and the challenges you faced during implementation.

Example

“I built a data pipeline using Apache Airflow for scheduling and orchestrating tasks, with data stored in Amazon S3 and processed using AWS Glue. One challenge was ensuring data quality, which I addressed by implementing validation checks at each stage of the pipeline.”

2. What is your experience with SQL and how have you used it in your projects?

SQL proficiency is crucial for a Data Engineer, and this question allows you to showcase your skills.

How to Answer

Provide specific examples of complex queries you’ve written, the databases you’ve worked with, and how your SQL skills contributed to project success.

Example

“I have extensive experience with SQL, particularly in PostgreSQL. In a recent project, I optimized a query that aggregated sales data, reducing the execution time from several minutes to under 30 seconds, which significantly improved reporting efficiency.”

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

Data quality is vital in data engineering, and this question evaluates your approach to maintaining it.

How to Answer

Discuss the methods you use for data validation, error handling, and monitoring data quality throughout the data lifecycle.

Example

“I implement data validation rules at the ingestion stage and regularly monitor data quality metrics. For instance, I set up alerts for anomalies in data patterns, which allows me to address issues proactively before they affect downstream processes.”

4. Describe a challenging technical problem you faced and how you solved it.

This question tests your problem-solving skills and ability to handle technical challenges.

How to Answer

Choose a specific problem, explain the context, the steps you took to resolve it, and the outcome.

Example

“I encountered a performance issue with a data processing job that was taking too long to complete. I analyzed the execution plan and identified that certain joins were inefficient. By rewriting the queries and indexing the relevant columns, I reduced the processing time by 70%.”

5. What tools and frameworks do you prefer for data processing and why?

This question allows you to discuss your familiarity with various data processing tools and frameworks.

How to Answer

Mention the tools you have experience with, why you prefer them, and how they fit into your workflow.

Example

“I prefer using Apache Spark for large-scale data processing due to its speed and ease of use with big data. I also appreciate its ability to handle both batch and streaming data, which makes it versatile for different project requirements.”

Project Experience

1. Share a project that you worked on that demonstrates your technical skills and critical thinking ability.

This question allows you to highlight a specific project and its impact.

How to Answer

Describe the project, your role, the technologies used, and the business problem it addressed.

Example

“I worked on a project to build a customer segmentation model using Python and scikit-learn. The model helped the marketing team target specific customer groups more effectively, resulting in a 20% increase in campaign ROI.”

2. How do you approach designing a data model for a new application?

This question assesses your understanding of data modeling principles.

How to Answer

Discuss your process for gathering requirements, designing the schema, and ensuring scalability.

Example

“I start by gathering requirements from stakeholders to understand the data needs. Then, I design the schema using normalization principles to reduce redundancy while ensuring it can scale as the application grows. I also consider indexing strategies to optimize query performance.”

3. What methodologies do you use for data engineering projects?

This question evaluates your familiarity with project management methodologies.

How to Answer

Mention the methodologies you’ve used (e.g., Agile, Scrum) and how they have influenced your work.

Example

“I typically use Agile methodologies for data engineering projects, which allows for iterative development and regular feedback. This approach has helped me adapt to changing requirements and deliver value incrementally.”

4. Describe a time when you had to collaborate with cross-functional teams.

Collaboration is key in data engineering, and this question assesses your teamwork skills.

How to Answer

Provide an example of a project where you worked with other teams, highlighting your communication and collaboration skills.

Example

“I collaborated with the product and marketing teams to develop a reporting dashboard. I facilitated meetings to gather requirements and ensured that the data provided was relevant and actionable, which led to improved decision-making across departments.”

5. How do you stay updated with the latest trends and technologies in data engineering?

This question gauges your commitment to professional development.

How to Answer

Discuss the resources you use to stay informed, such as blogs, courses, or conferences.

Example

“I regularly read industry blogs like Towards Data Science and participate in webinars. I also attend conferences like Strata Data Conference to network with other professionals and learn about emerging technologies in data engineering.”