Grammarly Data Engineer Interview Questions + Guide in 2025

Grammarly is a technology company that enhances communication through AI-driven writing assistance, helping millions worldwide improve their writing skills and clarity.

As a Data Engineer on the Data Engineering Platform team, you will play a critical role in building highly automated and low-latency datasets that enable seamless access to analytical data across Grammarly. This position involves creating and adjusting core data models, designing storage structures, and ensuring analytical data is made available shortly after real-world events. You will work on high-volume data processing, contribute to stream data frameworks, and develop infrastructure-as-code solutions that help your engineering team deploy software installations efficiently. Success in this role requires strong technical skills in Python, Scala, or Java, experience with relational databases, and a solid understanding of cloud services like AWS. Moreover, embodying Grammarly's EAGER values—ethical, adaptable, gritty, empathetic, and remarkable—will be essential for thriving in Grammarly's collaborative and innovative environment.

This guide will provide you with tailored insights and strategies to prepare effectively for your interview at Grammarly, ensuring you present yourself as a strong candidate who aligns with the company's values and expectations.

The interview process for a Data Engineer position at Grammarly is structured and thorough, reflecting the company's commitment to finding the right fit for their team. The process typically unfolds as follows:

1. Initial Recruiter Call

The first step involves a phone interview with a recruiter. This conversation is designed to introduce you to Grammarly's culture and values, as well as to discuss your background, skills, and motivations for applying. The recruiter will assess your fit for the role and provide insights into the next steps in the interview process.

2. Technical Screen

Following the initial call, candidates usually undergo a technical screening. This may involve a coding challenge or a technical interview focused on your proficiency in relevant programming languages such as Python, Scala, or Java. Expect questions that assess your understanding of data structures, algorithms, and system design principles.

3. Hiring Manager Interview

After successfully passing the technical screen, candidates typically have a conversation with the hiring manager. This interview focuses on your past experiences, your approach to problem-solving, and how you can contribute to the team. The hiring manager will also evaluate your alignment with Grammarly's values and culture.

4. Virtual Onsite Interviews

The next phase consists of a series of virtual onsite interviews, usually spanning multiple rounds. These interviews will cover a range of topics, including: - Technical Interviews: Expect to tackle coding problems, system design scenarios, and questions related to data modeling and database design. - Behavioral Interviews: These sessions will explore your past work experiences, teamwork, and how you handle challenges. Be prepared to discuss specific examples that demonstrate your skills and values. - Case Studies: You may be presented with real-world scenarios relevant to Grammarly's operations, requiring you to analyze data and propose solutions.

5. Final Interview

In some cases, candidates may have a final interview with senior leadership or team members. This round often focuses on cultural fit and your long-term vision for your role within the company. It’s an opportunity for you to ask questions about the team dynamics and the company's future direction.

Throughout the process, Grammarly emphasizes a collaborative and supportive atmosphere, ensuring that candidates feel comfortable and valued.

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

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

Embrace the Collaborative Culture

Grammarly places a strong emphasis on collaboration and teamwork. During your interviews, be prepared to discuss how you have worked effectively in teams, especially in remote settings. Highlight experiences where you contributed to group projects, mentored others, or facilitated discussions. This will demonstrate your alignment with Grammarly's values and your ability to thrive in their inclusive environment.

Prepare for a Structured Interview Process

The interview process at Grammarly is well-organized and thorough, often involving multiple rounds that assess both technical and behavioral competencies. Familiarize yourself with the structure of the interviews, which typically include technical screens, system design discussions, and behavioral interviews. Prepare to articulate your thought process clearly during technical challenges, as interviewers appreciate candidates who can communicate their reasoning effectively.

Showcase Your Technical Skills

As a Data Engineer, you will need to demonstrate proficiency in Python, Scala, or Java, as well as experience with database design and AWS. Brush up on your coding skills and be ready to solve problems related to data modeling, API design, and system architecture. Practice common data engineering scenarios and be prepared to discuss your past projects in detail, focusing on the impact of your work.

Highlight Your Problem-Solving Approach

Grammarly values analytical thinking and problem-solving abilities. During your interviews, be prepared to tackle case studies or hypothetical scenarios that require you to think critically about data challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey not just what you did, but how you approached the problem and the outcomes of your actions.

Be Authentic and Reflective

Grammarly's interviewers are known for their warmth and approachability. Use this to your advantage by being authentic in your responses. Share your career journey, including successes and challenges, and reflect on what you’ve learned from each experience. This will help you connect with your interviewers on a personal level and demonstrate your growth mindset.

Prepare for Behavioral Questions

Expect behavioral questions that assess your alignment with Grammarly's EAGER values (ethical, adaptable, gritty, empathetic, and remarkable). Prepare examples that illustrate how you embody these values in your work. Think about times when you faced challenges, adapted to change, or showed empathy towards colleagues, and be ready to discuss these experiences in detail.

Follow Up with Thoughtful Questions

At the end of your interviews, take the opportunity to ask insightful questions about the team, projects, and company culture. This not only shows your genuine interest in the role but also allows you to assess if Grammarly is the right fit for you. Consider asking about the team’s current challenges, how they measure success, or what a typical day looks like for a Data Engineer at Grammarly.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate who is not only technically capable but also a great cultural fit for Grammarly. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Grammarly. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your experience with data modeling, system design, and your approach to building scalable data solutions.

Technical Skills

1. Describe your experience with data lakes and how you would design a data lake architecture for a new project.

Understanding data lakes is crucial for this role, as you will be working with large volumes of data.

How to Answer

Discuss your familiarity with data lake concepts, the technologies you’ve used, and how you would approach designing a data lake architecture that meets specific business needs.

Example

“I have worked with AWS S3 to create a data lake that ingests data from various sources. I would design the architecture to include data ingestion pipelines using AWS Glue, ensuring that data is stored in a structured format for easy querying. Additionally, I would implement security measures and data governance policies to maintain data integrity.”

2. Can you explain the differences between relational databases and NoSQL databases? When would you choose one over the other?

This question tests your understanding of database technologies and their appropriate use cases.

How to Answer

Highlight the key differences between the two types of databases and provide scenarios where one would be more beneficial than the other.

Example

“Relational databases are structured and use SQL for querying, making them ideal for transactions and complex queries. NoSQL databases, on the other hand, are more flexible and can handle unstructured data, which is useful for applications requiring scalability and speed. I would choose NoSQL for a project with rapidly changing data requirements, such as a real-time analytics application.”

3. Describe a time when you optimized a data processing pipeline. What challenges did you face?

This question assesses your practical experience in improving data workflows.

How to Answer

Share a specific example, focusing on the problem, your approach to optimization, and the results achieved.

Example

“In my previous role, I noticed that our ETL process was taking too long due to inefficient data transformations. I implemented parallel processing and optimized our SQL queries, which reduced the processing time by 40%. The challenge was ensuring data integrity during the transition, but thorough testing helped mitigate risks.”

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

Data quality is critical for any data engineering role, and this question evaluates your approach to maintaining it.

How to Answer

Discuss the methods and tools you use to monitor and ensure data quality throughout the data lifecycle.

Example

“I implement data validation checks at various stages of the data pipeline, using tools like Apache Airflow for orchestration. Additionally, I set up alerts for anomalies and regularly review data quality metrics to identify and address issues proactively.”

5. What is your experience with cloud platforms, specifically AWS?

Given Grammarly's focus on AWS, this question gauges your familiarity with cloud services.

How to Answer

Detail your experience with AWS services relevant to data engineering, such as S3, Redshift, or Lambda.

Example

“I have extensive experience with AWS, particularly with S3 for data storage and Redshift for data warehousing. I have also utilized AWS Lambda for serverless data processing, which allowed us to scale our data ingestion processes efficiently.”

System Design

1. How would you design a system to handle real-time data processing?

This question tests your system design skills and understanding of real-time data architectures.

How to Answer

Outline the components of a real-time data processing system, including data sources, processing frameworks, and storage solutions.

Example

“I would design a system using Apache Kafka for data ingestion, allowing us to handle streams of data in real-time. For processing, I would use Apache Flink to perform transformations and aggregations, and store the results in a NoSQL database like DynamoDB for quick access.”

2. Can you walk us through your approach to building APIs for data access?

APIs are essential for data accessibility, and this question evaluates your experience in this area.

How to Answer

Discuss your experience with API design, including RESTful principles and any frameworks you’ve used.

Example

“I follow RESTful principles when designing APIs, ensuring they are stateless and use standard HTTP methods. I typically use Flask or FastAPI in Python to build APIs, and I focus on clear documentation and versioning to facilitate ease of use for developers.”

3. Describe a challenging data engineering project you worked on. What made it challenging and how did you overcome those challenges?

This question allows you to showcase your problem-solving skills and resilience.

How to Answer

Share a specific project, the challenges faced, and the strategies you employed to overcome them.

Example

“I worked on a project that required integrating data from multiple sources with varying formats. The challenge was ensuring data consistency and accuracy. I developed a robust data transformation layer using Apache NiFi, which allowed us to standardize the data before loading it into our data warehouse.”

4. How do you approach monitoring and logging in your data pipelines?

Monitoring is crucial for maintaining the health of data systems, and this question assesses your practices in this area.

How to Answer

Explain the tools and techniques you use for monitoring and logging data pipelines.

Example

“I use tools like Prometheus and Grafana for monitoring our data pipelines, setting up alerts for any failures or performance issues. Additionally, I implement logging at each stage of the pipeline to track data lineage and troubleshoot any issues that arise.”

5. What strategies do you use to ensure scalability in your data systems?

Scalability is vital for handling growing data volumes, and this question evaluates your foresight in system design.

How to Answer

Discuss the architectural choices and practices you employ to ensure systems can scale effectively.

Example

“I design systems with scalability in mind by using microservices architecture, which allows individual components to scale independently. I also leverage cloud services that can automatically scale based on demand, such as AWS Lambda for serverless processing.”

Behavioral Questions

1. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and prioritization skills.

How to Answer

Discuss your approach to prioritizing tasks based on urgency, impact, and deadlines.

Example

“I prioritize tasks by assessing their impact on the overall project goals and deadlines. I use project management tools like Jira to track progress and communicate with my team to ensure alignment on priorities.”

2. Describe a time when you had to collaborate with a cross-functional team. How did you ensure effective communication?

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

How to Answer

Share an example of a cross-functional project and how you facilitated communication among team members.

Example

“I worked on a project with the product and marketing teams to develop a new analytics feature. I scheduled regular check-ins and used collaborative tools like Slack and Confluence to keep everyone updated on progress and gather feedback, which helped us stay aligned and meet our deadlines.”

3. How do you handle feedback and criticism?

This question assesses your ability to accept and learn from feedback.

How to Answer

Discuss your perspective on feedback and how you use it to improve your work.

Example

“I view feedback as an opportunity for growth. When I receive criticism, I take the time to reflect on it and identify areas for improvement. I appreciate constructive feedback and often seek it out from peers to enhance my skills.”

4. Can you give an example of how you’ve mentored someone in your previous roles?

Mentorship is important for team growth, and this question evaluates your leadership skills.

How to Answer

Share a specific mentoring experience, focusing on your approach and the outcomes.

Example

“I mentored a junior data engineer by guiding them through their first project. I provided regular feedback and encouraged them to ask questions. As a result, they gained confidence in their skills and successfully delivered the project ahead of schedule.”

5. Why do you want to work at Grammarly?

This question assesses your motivation and alignment with the company’s values.

How to Answer

Express your enthusiasm for Grammarly’s mission and how your values align with the company culture.

Example

“I admire Grammarly’s commitment to improving communication and its focus on creating a supportive work environment. I believe my skills in data engineering can contribute to enhancing the user experience, and I’m excited about the opportunity to work with a talented team that values innovation and collaboration.”