Strava Data Engineer Interview Questions + Guide in 2025

Strava is the leading digital community for active individuals, empowering over 120 million athletes across more than 190 countries to connect and pursue their fitness goals.

As a Data Engineer at Strava, you will be a pivotal member of the Data Platform team, tasked with enhancing decision-making and product development through data insights. Your primary responsibilities will include developing robust data solutions that cater to both general and unique business needs. You’ll collaborate across various teams to identify opportunities where data can drive value, while also focusing on automating processes to minimize repetitive tasks. You should possess strong programming skills in languages like Python, Scala, or Java, and have a solid understanding of SQL queries and data pipelines. Experience with distributed data tools and cloud data warehouses will be crucial in helping you manage large datasets efficiently.

At Strava, the ideal candidate is someone who embodies empathy for users, fostering an inclusive team culture while maintaining high standards for code quality and data security. Your problem-solving abilities should shine, allowing you to creatively tackle challenges despite resource constraints. This guide will help you prepare for an interview by providing insights into what Strava values in a Data Engineer, equipping you with the knowledge needed to align your experiences with their expectations.

The interview process for a Data Engineer position at Strava is designed to assess both technical skills and cultural fit within the company. It typically consists of several stages that allow candidates to showcase their expertise and align with Strava's values.

1. Initial Recruiter Screen

The process begins with a phone interview with a recruiter, lasting about 30 minutes. This conversation focuses on your background, interest in the role, and understanding of Strava's mission. The recruiter will gauge your alignment with the company culture and your enthusiasm for the position. Be prepared to discuss your experience and how it relates to the role.

2. Technical Assessment

Following the initial screen, candidates may be required to complete a technical assessment, often involving SQL queries or coding challenges. This assessment is designed to evaluate your proficiency in relevant programming languages and your ability to work with data pipelines. Candidates should be ready to demonstrate their understanding of data engineering concepts and tools, as well as their problem-solving skills.

3. Interviews with Team Members

Successful candidates will then participate in multiple interviews with team members, typically ranging from three to four sessions. These interviews may include discussions with potential colleagues and managers from different teams. The focus will be on your technical skills, experience with distributed data tools, and your approach to collaboration and problem-solving. Expect questions that assess your ability to work with large datasets and your familiarity with cloud data warehouses.

4. Project Presentation

In some cases, candidates may be asked to complete a project prompt that requires presenting a solution to a panel. This project is intended to showcase your thought process, technical skills, and ability to communicate effectively. The prompt may focus on how you would approach a specific data challenge relevant to Strava's business needs.

5. Final Wrap-Up

The final stage of the interview process may involve wrap-up calls with HR or senior management. This is an opportunity for you to ask any remaining questions about the role, team dynamics, and company culture. It also allows the interviewers to assess your fit within the broader organizational context.

Throughout the process, candidates should be prepared for a mix of technical and behavioral questions that reflect Strava's commitment to collaboration, empathy, and high-quality work.

Next, let's explore the specific interview questions that candidates have encountered during their interviews at Strava.

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

Understand the Company Culture

Strava places a strong emphasis on community, inclusivity, and a people-first approach. Familiarize yourself with their mission to connect athletes and help them achieve their personal best. During the interview, demonstrate your alignment with these values by sharing experiences that highlight your commitment to teamwork, empathy, and fostering a positive environment. This will show that you not only understand Strava's culture but also that you can contribute to it.

Prepare for Technical Proficiency

As a Data Engineer, you will be expected to have a solid grasp of SQL, Python, and distributed data tools. Brush up on your SQL skills, particularly in the context of data pipelines, and be ready to discuss your experience with cloud data warehouses and data processing frameworks. Consider preparing a few examples of how you've used these technologies to solve real-world problems, as this will help you articulate your technical expertise effectively.

Clarify Expectations

Given the feedback from previous candidates about mismatches between role expectations and interview questions, it’s crucial to clarify the specifics of the role during your conversations. Don’t hesitate to ask the interviewers about the key responsibilities and challenges of the position. This not only shows your interest but also helps you tailor your responses to align with what they are truly looking for.

Showcase Problem-Solving Skills

Strava values creative and efficient problem-solving. Be prepared to discuss how you approach challenges, particularly in data engineering contexts. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on how you identified a problem, the steps you took to address it, and the outcomes of your actions. This will demonstrate your analytical thinking and ability to deliver results under constraints.

Engage with Your Interviewers

Candidates have noted that Strava's interviewers are friendly and welcoming. Use this to your advantage by engaging in a two-way conversation. Ask insightful questions about the team dynamics, ongoing projects, and how data engineering contributes to Strava's mission. This not only shows your enthusiasm for the role but also helps you gauge if the team is the right fit for you.

Follow Up Professionally

After your interviews, consider sending a thoughtful follow-up email to express your gratitude for the opportunity and reiterate your interest in the role. This can help you stand out, especially in a company where communication has been noted as an area for improvement. A well-crafted follow-up can leave a positive impression and keep you on their radar.

By preparing thoroughly and aligning your approach with Strava's values and expectations, you can position 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 Strava. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of data systems, as well as your capacity to work collaboratively within a team. Be prepared to discuss your experience with data technologies, coding, and how you can contribute to Strava's mission of enriching the athlete experience through data.

Technical Skills

1. Can you explain your experience with SQL and how you have used it in data pipelines?

Understanding SQL is crucial for a Data Engineer role, as it is often used for querying and managing data.

How to Answer

Discuss specific projects where you utilized SQL, focusing on the complexity of the queries and the outcomes achieved.

Example

“In my previous role, I developed complex SQL queries to extract and transform data from multiple sources into a centralized data warehouse. This allowed the analytics team to generate insights more efficiently, ultimately improving our reporting speed by 30%.”

2. Describe a project where you implemented a distributed data processing tool like Spark or Kafka.

Experience with distributed systems is essential for handling large datasets.

How to Answer

Highlight the project scope, your role, and the impact of using these tools on data processing efficiency.

Example

“I led a project where we used Apache Spark to process streaming data from IoT devices. By implementing Spark Streaming, we reduced data processing time from hours to minutes, enabling real-time analytics for our users.”

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

Data quality is critical in any data-driven organization.

How to Answer

Discuss your strategies for validating data, handling errors, and maintaining data integrity throughout the pipeline.

Example

“I implement automated data validation checks at various stages of the ETL process. This includes schema validation and anomaly detection, which helps catch issues early and ensures that the data remains reliable for end-users.”

4. What is your experience with cloud data warehouses like Snowflake or BigQuery?

Familiarity with cloud solutions is increasingly important in data engineering.

How to Answer

Share specific examples of how you have utilized these platforms in your work.

Example

“I have worked extensively with Google BigQuery to manage large datasets. I optimized our data storage and querying processes, which resulted in a 40% reduction in costs and improved query performance.”

5. Can you describe your experience with programming languages relevant to data engineering, such as Python or Scala?

Programming skills are essential for building data pipelines and automation.

How to Answer

Mention specific projects or tasks where you used these languages, focusing on your contributions.

Example

“I primarily use Python for data manipulation and automation tasks. In a recent project, I developed a Python script that automated the data ingestion process, reducing manual effort by 50% and allowing the team to focus on analysis.”

Problem-Solving and Collaboration

1. Describe a time when you faced a significant challenge in a data project. How did you overcome it?

Problem-solving skills are vital in data engineering.

How to Answer

Outline the challenge, your approach to resolving it, and the outcome.

Example

“During a data migration project, we encountered unexpected data discrepancies. I organized a cross-functional team to investigate the issue, and we identified a misalignment in data formats. By standardizing the formats, we successfully completed the migration on time.”

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

Time management and prioritization are key in a fast-paced environment.

How to Answer

Explain your approach to managing competing priorities and ensuring project deadlines are met.

Example

“I use a combination of project management tools and regular check-ins with stakeholders to prioritize tasks. This helps me stay aligned with team goals and ensures that critical projects receive the attention they need.”

3. Can you give an example of how you have collaborated with other teams to achieve a data-related goal?

Collaboration is essential in a role that interacts with various functions.

How to Answer

Discuss a specific instance where teamwork led to a successful outcome.

Example

“I collaborated with the marketing team to develop a dashboard that visualized user engagement metrics. By working closely with them to understand their needs, we created a tool that provided actionable insights, leading to a 20% increase in campaign effectiveness.”

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

Continuous learning is important in a rapidly evolving field.

How to Answer

Share your methods for keeping your skills current, such as attending workshops, online courses, or following industry leaders.

Example

“I regularly attend data engineering meetups and webinars, and I’m an active member of several online communities. I also take online courses to deepen my knowledge of emerging technologies, ensuring I can apply the latest best practices in my work.”

5. What motivates you to work in data engineering, particularly at Strava?

Understanding your motivation can help interviewers gauge your fit within the company culture.

How to Answer

Express your passion for data and how it aligns with Strava’s mission.

Example

“I’m passionate about using data to enhance user experiences, and Strava’s commitment to connecting athletes resonates with me. I’m excited about the opportunity to contribute to a platform that empowers people to achieve their personal best.”