Acumen Data Engineer Interview Questions + Guide in 2025

Acumen is a leading organization focused on providing data-driven insights to improve healthcare policy and public health outcomes.

As a Data Engineer at Acumen, you will play a critical role in developing and maintaining the data architecture that supports the company's health policy initiatives. Your key responsibilities will include designing and implementing data pipelines, ensuring data integrity, and collaborating with data scientists and analysts to create robust analytical frameworks. The ideal candidate will possess strong skills in SQL and Python, with a solid understanding of algorithms and data structures. A passion for health policy and the ability to work collaboratively across teams are essential traits for success in this role.

This guide will help you prepare for your interview by providing insights into the skills and experiences that Acumen values, allowing you to confidently articulate your qualifications and fit for the position.

The interview process for a Data Engineer at Acumen is structured and involves multiple stages designed to assess both technical skills and cultural fit.

1. Application and Initial Screening

The process begins with submitting an application, which may include a resume, cover letter, and any relevant transcripts or writing samples. Following this, candidates typically undergo an initial phone screening with a recruiter. This conversation focuses on the candidate's background, experience, and motivation for applying to Acumen, as well as a general discussion about the role and the company.

2. Technical Assessment

After the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home exercise where candidates analyze a dataset and answer specific questions related to their findings. The assessment is designed to evaluate the candidate's analytical skills, familiarity with data manipulation, and ability to derive insights from data.

3. Phone Interview

Once the technical assessment is completed, candidates usually participate in a phone interview. This interview often involves discussing the results of the technical assessment in detail, as well as exploring the candidate's previous projects and experiences. Interviewers may ask about specific tools and frameworks the candidate has used, as well as their approach to problem-solving in data engineering contexts.

4. Onsite Interview

The final stage of the interview process is typically an onsite interview, which may also be conducted virtually. This stage usually consists of multiple rounds of interviews with various team members, including technical and behavioral interviews. Candidates can expect to face questions related to algorithms, data structures, and system design, as well as discussions about their past projects and how they relate to the work at Acumen. Additionally, candidates may be given a logic or math test to assess their quantitative skills.

Throughout the process, candidates should be prepared to discuss their technical expertise, particularly in areas such as SQL and algorithms, as well as their experience with data analysis and engineering practices.

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 Interview Structure

The interview process at Acumen typically involves multiple stages, including a written test, phone interviews, and onsite interviews. Familiarize yourself with this structure and prepare accordingly. Expect to discuss your past projects in detail, as interviewers often focus on your experience and how it relates to the role. Be ready to articulate your thought process and the methodologies you used in your previous work.

Prepare for Technical Assessments

Given the emphasis on data analysis and logic, brush up on your analytical skills. You may encounter a logic test or a data analysis exercise, so practice solving problems that require critical thinking and statistical reasoning. Familiarize yourself with common data structures and algorithms, as basic questions on these topics may arise. Additionally, be prepared to discuss your approach to data analysis and the tools you have used.

Showcase Your Projects

Acumen values candidates who can effectively communicate their project experiences. Be prepared to discuss specific projects in detail, including the datasets you worked with, the challenges you faced, and the outcomes of your analyses. Highlight your contributions and the impact of your work, especially in relation to healthcare or policy, as this aligns with Acumen's mission.

Engage with Interviewers

While some candidates have reported a lack of feedback during interviews, it’s important to engage with your interviewers. Ask clarifying questions if you feel the conversation is one-sided, and express genuine interest in their work and the company. This not only demonstrates your enthusiasm but also helps you gauge if Acumen is the right fit for you.

Be Patient and Follow Up

The interview process can be lengthy, with some candidates experiencing delays in communication. If you haven’t heard back within the expected timeframe, don’t hesitate to follow up politely. This shows your continued interest in the position and can help keep you on the interviewers' radar.

Embrace the Company Culture

Acumen has a collaborative and mission-driven culture. Show that you align with their values by discussing your interest in healthcare and policy. Be prepared to articulate why you want to work at Acumen specifically, and how your background and skills can contribute to their goals.

Stay Positive and Professional

Regardless of your experience with the interview process, maintain a positive and professional demeanor. Candidates have reported mixed experiences, but your attitude can set you apart. Approach each interaction with respect and professionalism, and remember that every interview is an opportunity to learn and grow.

By following these tips, you can navigate the interview process at Acumen with confidence and increase your chances of success. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Acumen. The interview process will likely focus on your technical skills, project experience, and understanding of data structures and algorithms. Be prepared to discuss your past projects in detail, as well as demonstrate your analytical thinking and problem-solving abilities.

Technical Skills

1. Can you explain the difference between a primary key and a foreign key in SQL?

Understanding database relationships is crucial for a Data Engineer.

How to Answer

Discuss the definitions of primary and foreign keys, and explain their roles in maintaining data integrity and establishing relationships between tables.

Example

“A primary key uniquely identifies each record in a table, ensuring that no two rows have the same value. A foreign key, on the other hand, is a field in one table that links to the primary key of another table, establishing a relationship between the two tables.”

2. Describe a time when you optimized a SQL query. What steps did you take?

Optimization is key in data engineering to improve performance.

How to Answer

Detail the specific query you optimized, the challenges you faced, and the techniques you used to enhance its performance.

Example

“I had a query that was running slowly due to multiple joins. I analyzed the execution plan, identified unnecessary joins, and added indexes to the relevant columns. This reduced the query execution time from several minutes to under a second.”

3. What are window functions in SQL, and when would you use them?

Window functions are essential for performing calculations across a set of table rows related to the current row.

How to Answer

Explain what window functions are and provide examples of scenarios where they are beneficial.

Example

“Window functions allow you to perform calculations across a set of rows related to the current row without collapsing the result set. For instance, I used a window function to calculate a running total of sales over time while still displaying individual sales records.”

4. How do you handle missing or corrupted data in a dataset?

Data quality is critical in engineering roles.

How to Answer

Discuss your approach to identifying and handling missing or corrupted data, including any tools or techniques you use.

Example

“I typically start by analyzing the dataset to identify patterns of missing data. Depending on the context, I might choose to impute missing values using the mean or median, or I may remove records with excessive missing data to maintain the integrity of the analysis.”

5. Can you explain the concept of normalization and denormalization?

Understanding these concepts is vital for database design.

How to Answer

Define both terms and explain their importance in database management.

Example

“Normalization is the process of organizing data to reduce redundancy and improve data integrity, typically through the creation of multiple related tables. Denormalization, on the other hand, involves combining tables to improve read performance, which can be beneficial in data warehousing scenarios.”

Algorithms and Data Structures

1. What is the time complexity of a binary search?

Understanding algorithm efficiency is crucial for a Data Engineer.

How to Answer

Explain the concept of time complexity and provide the time complexity of binary search.

Example

“The time complexity of a binary search is O(log n) because it divides the search interval in half with each iteration, making it much more efficient than a linear search, which has a time complexity of O(n).”

2. Can you describe a situation where you had to use a data structure like a stack or queue?

Practical application of data structures is important in engineering.

How to Answer

Provide a specific example of how you used a stack or queue in a project.

Example

“In a project where I was implementing a web scraper, I used a stack to keep track of the URLs to visit. This allowed me to efficiently manage the order of requests and ensure that I didn’t revisit URLs.”

3. How would you implement a hash table?

Hash tables are fundamental data structures in programming.

How to Answer

Discuss the basic principles of hash tables and how you would implement one.

Example

“I would create an array to store the data and use a hash function to convert keys into array indices. To handle collisions, I would implement chaining or open addressing to ensure that all entries can be stored and retrieved efficiently.”

4. Explain the concept of recursion and provide an example of a problem that can be solved using recursion.

Recursion is a common programming technique.

How to Answer

Define recursion and describe a problem that can be solved recursively.

Example

“Recursion is a method where a function calls itself to solve smaller instances of the same problem. A classic example is calculating the factorial of a number, where n! = n * (n-1)!. This can be implemented easily with a recursive function.”

5. What is the difference between depth-first search (DFS) and breadth-first search (BFS)?

Understanding these algorithms is essential for data traversal.

How to Answer

Explain the differences in approach and use cases for both algorithms.

Example

“DFS explores as far down a branch as possible before backtracking, making it useful for problems like pathfinding in mazes. BFS, on the other hand, explores all neighbors at the present depth prior to moving on to nodes at the next depth level, which is ideal for finding the shortest path in unweighted graphs.”

Project Experience

1. Describe a data engineering project you are particularly proud of.

Your project experience is a key focus area.

How to Answer

Highlight the project’s objectives, your role, and the impact it had.

Example

“I led a project to build a data pipeline for processing healthcare data. I designed the ETL process, which improved data accessibility for analysts by 40%. This project not only streamlined our data processing but also enhanced the quality of insights derived from the data.”

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

Data quality is paramount in engineering roles.

How to Answer

Discuss the methods you use to maintain data quality throughout the project lifecycle.

Example

“I implement data validation checks at various stages of the ETL process, including schema validation and anomaly detection. Additionally, I conduct regular audits and encourage feedback from data users to continuously improve data quality.”

3. Can you walk us through your experience with cloud platforms like AWS or Azure?

Familiarity with cloud services is often required.

How to Answer

Detail your experience with specific cloud services and how you utilized them in your projects.

Example

“I have extensive experience with AWS, particularly with services like S3 for data storage and Redshift for data warehousing. In a recent project, I migrated our on-premise data warehouse to Redshift, which improved query performance and reduced costs significantly.”

4. What tools and technologies do you prefer for data visualization?

Data visualization is an important aspect of data engineering.

How to Answer

Discuss the tools you are familiar with and your reasons for using them.

Example

“I prefer using Tableau for data visualization due to its user-friendly interface and powerful capabilities for creating interactive dashboards. I also use Python libraries like Matplotlib and Seaborn for more customized visualizations in my data analysis projects.”

5. How do you approach collaboration with data scientists and analysts?

Collaboration is key in data-driven environments.

How to Answer

Explain your approach to working with cross-functional teams.

Example

“I prioritize open communication and regular check-ins with data scientists and analysts to understand their data needs. I also ensure that the data infrastructure I build is user-friendly and well-documented, making it easier for them to access and utilize the data effectively.”