Afresh Data Engineer Interview Questions + Guide in 2025

Afresh is dedicated to eliminating food waste and making fresh food accessible, leveraging A.I.-powered solutions that optimize grocery operations.

As a Data Engineer at Afresh, you will play a crucial role in developing and enhancing the company's analytics platform. This position involves improving the data architecture to ensure reliable and accessible data for various use cases, collaborating closely with cross-functional teams including product managers, data scientists, and software engineers. You will be responsible for evolving the data warehouse schema, solidifying the transformation architecture, and establishing data governance practices that meet both internal and external analytics requirements.

The ideal candidate will have extensive experience (6+ years) in data engineering or a related field, strong SQL skills, and a deep understanding of data engineering concepts such as Data Mesh, Data Lake, and Data Warehouse. Exceptional communication and leadership skills are essential, as you will mentor junior engineers and facilitate collaboration across teams. Experience with SQL-driven transformation libraries and setting up CI/CD pipelines will be beneficial for success in this role.

This guide aims to prepare you for your interview by providing insights into the expectations and competencies valued at Afresh, allowing you to approach the interview process with confidence and clarity.

The interview process for a Data Engineer role at Afresh is designed to assess both technical skills and cultural fit within the company. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and alignment with Afresh's mission.

1. Initial Contact

The process usually begins with an initial outreach from a recruiter or a hiring manager. This first contact may involve a brief conversation to discuss your background, experience, and interest in the role. It serves as a preliminary assessment of your fit for the company culture and the specific position.

2. Technical Assessment

Following the initial contact, candidates are often required to complete a technical assessment. This may include an online coding test that focuses on practical skills relevant to the role, such as SQL proficiency and data manipulation. The assessment is designed to evaluate your problem-solving abilities and familiarity with data engineering concepts. Candidates should expect straightforward tasks that reflect real-world scenarios rather than traditional algorithmic challenges.

3. Technical Interview

After successfully completing the technical assessment, candidates typically participate in a technical interview. This interview may involve coding exercises conducted in a collaborative environment, such as a shared coding platform. You may be asked to solve problems related to data architecture, data quality, and system design. This stage is crucial for demonstrating your technical expertise and ability to think critically under pressure.

4. System Design Interview

Candidates may then move on to a system design interview, where they will be asked to design a data system or architecture that meets specific requirements. This interview assesses your understanding of data engineering principles, including OLTP vs. OLAP design, data governance, and the use of ELT paradigms. Be prepared to discuss your design choices and how they align with best practices in data engineering.

5. Cross-Functional Interviews

The final stage often includes interviews with cross-functional team members, such as product managers and data scientists. These discussions focus on collaboration and communication skills, as well as your ability to understand and address the data needs of various stakeholders. This is an opportunity to showcase your interpersonal skills and how you can contribute to a team-oriented environment.

6. Culture Fit Interview

Lastly, candidates may have a culture fit interview, which assesses alignment with Afresh's values and mission. This conversation may involve discussing your motivations, work style, and how you approach challenges in a team setting. The goal is to ensure that you resonate with the company's commitment to reducing food waste and making fresh food accessible.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

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

Understand the Company’s Mission and Values

Afresh is dedicated to reducing food waste and making fresh food accessible. Familiarize yourself with their mission and how their analytics platform supports this goal. Be prepared to discuss how your experience aligns with their values of proactivity, kindness, candor, and humility. Showing genuine enthusiasm for their mission can set you apart from other candidates.

Prepare for Technical Assessments

Expect a mix of coding assessments and system design interviews. The coding tests may not follow traditional algorithmic questions, so focus on practical applications of your skills. Brush up on SQL, Python, and relevant technologies like dbt and Airflow. Practice building data models and implementing caching strategies, as these are likely to be relevant to the role. Remember, the assessments are designed to evaluate your problem-solving skills in a real-world context.

Emphasize Collaboration and Communication Skills

Given the collaborative nature of the role, be ready to demonstrate your ability to work cross-functionally. Share examples of how you have successfully collaborated with product managers, data scientists, and other engineers in the past. Highlight your communication skills, especially in explaining complex technical concepts to non-technical stakeholders. This will show that you can bridge the gap between technical and non-technical teams.

Showcase Your Experience with Data Governance

As a Data Engineer at Afresh, you will be responsible for evolving data quality and governance processes. Be prepared to discuss your experience in establishing data governance frameworks and ensuring data integrity. Share specific examples of how you have improved data quality in previous roles, and be ready to discuss the tools and methodologies you used.

Be Ready for Behavioral Questions

Expect questions that assess your cultural fit within the company. Afresh values a human-first approach, so be prepared to discuss how you handle challenges, provide feedback, and mentor others. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on how your actions contributed to team success and fostered a positive work environment.

Follow Up with Thoughtful Questions

At the end of the interview, ask insightful questions that reflect your understanding of the company and the role. Inquire about the team dynamics, the challenges they face in data governance, or how they measure the success of their analytics platform. This not only shows your interest but also helps you gauge if the company is the right fit for you.

By following these tips and preparing thoroughly, you can present yourself as a strong candidate who is not only technically proficient but also aligned with Afresh's mission and values. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Afresh. The interview process will likely assess your technical skills, problem-solving abilities, and your capacity to collaborate with cross-functional teams. Be prepared to demonstrate your knowledge of data architecture, SQL, and data governance, as well as your experience with relevant technologies.

Technical Skills

1. Can you explain the differences between OLTP and OLAP database design?

Understanding the distinctions between these two types of database systems is crucial for a data engineer, especially in the context of analytics.

How to Answer

Discuss the primary functions of OLTP (Online Transaction Processing) systems, which are optimized for transaction-oriented applications, and OLAP (Online Analytical Processing) systems, which are designed for complex queries and data analysis.

Example

“OLTP systems are designed for managing transactional data and are optimized for speed and efficiency in processing a large number of short online transactions. In contrast, OLAP systems are optimized for read-heavy operations and complex queries, allowing for data analysis and reporting across large datasets.”

2. Describe your experience with SQL-driven transform libraries like dbt or sqlmesh.

This question assesses your familiarity with modern data transformation tools that support ELT paradigms.

How to Answer

Highlight your experience with these tools, focusing on how you have used them to create and manage data transformations, as well as any CI/CD pipelines you have set up.

Example

“I have over a year of experience using dbt to manage our data transformations. I set up CI/CD pipelines that ensure our transformations are tested and deployed efficiently, which has significantly improved our data quality and governance processes.”

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

Data quality is a critical aspect of data engineering, and interviewers will want to know your approach to maintaining it.

How to Answer

Discuss the strategies you employ to monitor and validate data quality, such as automated testing, data profiling, and establishing data governance frameworks.

Example

“I implement automated testing for our data pipelines to catch errors early in the process. Additionally, I conduct regular data profiling to identify anomalies and ensure that our data governance policies are adhered to, which helps maintain high data quality.”

4. Can you walk us through a data architecture project you’ve worked on?

This question allows you to showcase your hands-on experience in designing and implementing data architectures.

How to Answer

Provide a brief overview of the project, your role, the technologies used, and the impact it had on the organization.

Example

“I led a project to redesign our data warehouse architecture to support real-time analytics. We migrated to a Snowflake-based solution, which improved our query performance by 50% and allowed our data science team to access up-to-date data for their models.”

5. What is your experience with data governance processes?

Data governance is essential for ensuring data integrity and compliance, and your experience in this area will be evaluated.

How to Answer

Discuss your understanding of data governance principles and any frameworks or processes you have implemented in previous roles.

Example

“I have implemented data governance processes that include defining data ownership, establishing data quality metrics, and creating documentation for data lineage. This has helped our teams understand data sources and maintain compliance with industry standards.”

Collaboration and Communication

1. How do you approach collaboration with cross-functional teams?

Collaboration is key in a data engineering role, and interviewers will want to know how you work with others.

How to Answer

Share your strategies for effective communication and collaboration, emphasizing your ability to understand the needs of different stakeholders.

Example

“I prioritize regular check-ins with cross-functional teams to understand their data needs. I also use collaborative tools to share insights and updates, ensuring everyone is aligned and can provide input on data-related projects.”

2. Describe a time when you had to mentor a junior engineer.

Mentoring is an important aspect of the role, and your ability to guide others will be assessed.

How to Answer

Provide an example of a mentoring experience, focusing on how you supported the junior engineer’s growth and development.

Example

“I mentored a junior engineer by pairing with them on a data pipeline project. I guided them through the design process and encouraged them to take ownership of specific tasks, which helped them build confidence and improve their technical skills.”

3. How do you handle conflicts within a team?

Conflict resolution skills are essential for maintaining a productive work environment.

How to Answer

Discuss your approach to addressing conflicts, emphasizing open communication and finding common ground.

Example

“When conflicts arise, I believe in addressing them directly and openly. I facilitate discussions to understand each party's perspective and work towards a solution that aligns with our project goals, ensuring that everyone feels heard.”

4. Can you give an example of a successful project where you collaborated with product managers?

This question assesses your ability to work with product teams to deliver data-driven solutions.

How to Answer

Share a specific project example, highlighting your role and the outcome of the collaboration.

Example

“I collaborated with product managers to develop a reporting tool that provided insights into customer behavior. By understanding their requirements and iterating based on feedback, we delivered a product that significantly improved decision-making for our marketing team.”

5. How do you communicate complex technical concepts to non-technical stakeholders?

Effective communication is vital, especially when dealing with stakeholders who may not have a technical background.

How to Answer

Discuss your strategies for simplifying complex concepts and ensuring clarity in your communication.

Example

“I focus on using analogies and visual aids to explain complex technical concepts. For instance, when discussing data pipelines, I compare them to water flowing through pipes, which helps non-technical stakeholders understand the flow and transformation of data.”