Interviews for data engineering managers assess two important skills:
First and foremost, data engineering managers must be talented engineers, but that isn’t sufficient on its own to manage a team. They must also be strong leaders that can direct a team of engineers to produce results by inspiring or incentivizing their colleagues.
Due to the need for both of these skills, the interview will therefore be a mix of technical questions and behavioral approaches, focusing on a candidate’s people leadership and engineering management experience.
Data engineering managers are responsible for:
Interviews for data engineer manager roles typically include 2-3 rounds of behavioral and leadership questions. These rounds will represent the day-to-day responsibilities of a data engineering manager.
In addition, these interviews typically include 1-2 technical rounds, which are geared towards system design case studies but may include SQL, system design, and/or machine learning questions. We will include technical questions to expect below, but you can also check out data engineering interview questions.
The process for management interviews is standard across the industry. They involve a short recruiter call, followed by a hiring manager screen and technical screen. If you pass this initial screening process, you’ll be invited to an on-site interview.
On-site interviews for data engineer management roles typically include 5 rounds, including:
Behavioral interviews are discussion-based and assess leadership and management philosophies. The interviewer will want to see how you approach and address different types of real-world scenarios. These questions are closely linked to your future responsibilities:
Example: Describe a time when you had to lead your team through a challenging data engineering project. How did you ensure everyone was aligned and motivated to accomplish the project goals?
Example: Explain a situation where you had to communicate complex data engineering concepts to non-technical stakeholders. How did you ensure they understood the importance and implications of the project?
Example: Describe a data engineering project where you faced significant constraints, such as limited resources or a tight deadline. How did you overcome these challenges and deliver a successful outcome?
Example: Walk us through a past data engineering project where you had to align your team’s efforts with the company’s strategic goals. How did you ensure that your team’s work contributed to the overall success of the organization?
One tip: Be selective about the experiences you choose to describe. Your answers should illustrate your management experience and skills. For example, describing a disagreement over strategic direction rather than a simple coding dispute would be a more appropriate response to the question above.
You should also use a simple framework to structure your response. With an approach like STAR, you would:
Describe a complex data engineering project you worked on. What were the biggest challenges you faced?
In a previous role, I managed a team that had been tasked with building a real-time streaming data processing system to support a high-traffic web application. The biggest challenge we faced was building a system that could handle the high volume of data, while also maintaining low latency. To overcome this, we implemented a microservices architecture and used Apache Kafka as the messaging system. We also had to ensure the system was scalable and fault-tolerant, so we implemented redundancy and monitoring mechanisms to detect issues and quickly recover from them.
How do you manage conflicting priorities and stakeholder expectations when working on multiple projects?
When working on multiple projects, I prioritize based on the impact each project has on the business and the resources available. I work closely with stakeholders to understand their needs and expectations, then set realistic timelines and milestones. If conflicts arise, I communicate openly and transparently with all stakeholders to ensure that everyone is aware of the situation.
More Behavioral Questions:
Click on the link for more behavioral data science questions at Interview Query.
Describe a time when you had to motivate your team to achieve a challenging goal. What steps did you take to build morale and ensure your team was successful?
As a data engineering manager at my previous company, I lead a team to migrate our data warehouse to a new platform within a tight timeline. To motivate my team, I first made sure they understood the importance of the project and how it would benefit the company. Then, I broke down the project into smaller, achievable milestones and created a detailed plan with clear deadlines for each milestone.
I also encouraged my team to collaborate and share ideas, and I made sure to recognize and reward their hard work and accomplishments along the way. Ultimately, by breaking the project down into smaller pieces, providing regular feedback and recognition, and fostering a positive team culture, we were able to successfully complete the full migration on time.
How do you prioritize technical debt and ensure that it doesn’t negatively impact your team’s ability to deliver new features and projects?
I prioritize technical debt by working closely with my team to identify areas of the codebase that require attention. I encourage my team to be proactive about addressing technical debt, and we regularly set aside time to tackle these issues.
To ensure that technical debt doesn’t negatively impact our ability to deliver new features and projects, I also work to balance these efforts with our other priorities. I prioritize high-impact technical debt items and schedule them alongside new feature development. I also regularly assess and re-prioritize our backlog of technical debt items to ensure that we are addressing the most critical items first.
More Example Leadership Questions
In management interviews, you can expect medium-to-hard technical questions. During the technical screening, these might include smaller case studies or ETL SQL questions. During an on-site, the technical questions are generally a multi-step data engineering case study.
There’s a way to approach technical questions, and our data engineering learning path provides frameworks for all of the types of questions you’re most likely to face.
Let’s say that you’re in charge of getting payment data into your internal data warehouse. How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?
See a mock interview answer for this question.
You’re in charge of designing the end-to-end architecture of an e-commerce platform like Amazon. What clarifying questions would you ask? What kind of end-to-end architecture would you design for this company (both for ETL and reporting)?
More context: Let’s say you work for an e-commerce company. Vendors can send products to the company’s warehouse to be listed on the website.
Users can order any in-stock products and submit returns for refunds if they’re not satisfied. The front end of the website includes a vendor portal that provides sales data in daily, weekly, monthly, quarterly, and yearly intervals.
See a mock interview answer for this question.
If you’d like more practice, check out more data engineering interview questions at Interview Query.
For data engineering manager candidates, here are some unique tips to help you succeed in your interviews:
Before any data engineering interview, explore our data engineering learning path. This multi-course path teaches best practices for answering technical and case study questions in data engineering interviews and will help you prepare and refine your interviewing skills.