Step Data Engineer Interview Questions + Guide in 2025

Step is a next-generation financial services company focused on improving the financial future of teens and young adults through innovative banking solutions.

In the role of Data Engineer at Step, you will be responsible for enabling and accelerating data analytics and data science projects. Key responsibilities include designing and building data pipelines, maintaining data transformations, and creating dashboards to track essential business metrics. You will be expected to possess strong analytical skills, with a proficiency in SQL and experience with modern programming languages, as well as data visualization tools. The ideal candidate will communicate effectively with both technical and non-technical audiences and have a bias towards developing simple, efficient solutions. A strong background in analytics engineering, particularly in building reusable and efficient datasets, is essential for success in this position.

This guide will help you prepare for your interview by outlining the critical skills and responsibilities associated with the Data Engineer role at Step, giving you a clear understanding of what to focus on during your preparation.

The interview process for a Data Engineer role at Step is designed to thoroughly assess both technical and interpersonal skills, ensuring candidates are well-suited for the dynamic environment of a fast-growing financial services company. The process typically unfolds in several structured stages:

1. Initial Screening

The process begins with an initial screening conducted by a recruiter. This 30-minute phone call focuses on understanding your background, experience, and motivation for applying to Step. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates undergo a technical assessment, which may be conducted via a video call. This stage typically includes two technical interviews, each lasting around 45 minutes. Interviewers will present coding challenges that test your proficiency in data structures and algorithms, particularly focusing on SQL and programming languages like Python. Expect to solve problems related to hashmaps, strings, and linked lists, as well as questions on data pipelines and transformations.

3. Behavioral Interview

After the technical assessments, candidates will participate in a behavioral interview. This round aims to evaluate your soft skills, communication abilities, and cultural fit within the Step team. Interviewers will ask about your past experiences, how you handle challenges, and your approach to teamwork and collaboration. Be prepared to discuss specific projects and how your contributions made an impact.

4. Case Study or Practical Exercise

In some instances, candidates may be asked to complete a case study or practical exercise. This could involve analyzing a dataset and presenting your findings, demonstrating your analytical skills and ability to derive actionable insights. Interviewers will be interested in your thought process, methodologies, and the rationale behind your decisions.

5. Final Interview

The final stage of the interview process may include a meeting with senior leadership or team members. This round often focuses on assessing your alignment with Step's mission and values, as well as your long-term career goals. It’s an opportunity for you to ask questions about the company’s direction and how you can contribute to its success.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

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

Master Data Structures and Algorithms

Given the emphasis on problem-solving skills, particularly with data structures and algorithms, it's crucial to brush up on your knowledge of hashmaps, strings, linked lists, trees, and graphs. Practice solving medium-level LeetCode questions to familiarize yourself with the types of problems you may encounter. Be prepared to explain your thought process clearly as you work through these problems, as interviewers will be assessing not just your final answer but also your approach to problem-solving.

Showcase Your Technical Proficiency

As a Data Engineer, a strong command of SQL is essential. Be ready to discuss your experience with SQL in detail, including complex queries, data transformations, and performance optimization. Additionally, familiarize yourself with modern programming languages relevant to the role, such as Python. You may be asked to demonstrate your coding skills live, so practice coding in a collaborative environment, like Google Docs, to simulate the interview experience.

Prepare for Behavioral Questions

Expect a mix of technical and behavioral questions. Prepare to discuss your past experiences, particularly those that highlight your ability to work autonomously and collaboratively within cross-functional teams. Be ready to articulate your project management philosophy and how you approach challenges in a team setting. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your contributions.

Understand the Company Culture

Step is focused on empowering the next generation, so it’s important to align your values with the company’s mission. Research the company’s initiatives and be prepared to discuss how your skills and experiences can contribute to their goals. Show enthusiasm for their mission to improve financial literacy among teens and young adults, and be ready to share your thoughts on how data can drive impactful decisions in this space.

Communicate Clearly and Effectively

Given the role's requirement to communicate with both technical and non-technical audiences, practice explaining complex technical concepts in simple terms. This skill will be crucial during your interviews, especially when discussing your past projects or data analysis methodologies. Be prepared to walk interviewers through your thought process and the rationale behind your decisions.

Leverage Resources and Feedback

Take advantage of any resources provided by the company during the interview process. If you receive feedback from recruiters or interviewers, use it to refine your approach. This shows your willingness to learn and adapt, which is a valuable trait in a fast-paced environment like Step.

Stay Calm and Engaged

Interviews can be intense, but maintaining a calm demeanor will help you think clearly and perform better. Engage with your interviewers by asking insightful questions about the team, projects, and company culture. This not only demonstrates your interest in the role but also helps you assess if Step is the right fit for you.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Data Engineer role at Step. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Step. The interview process will likely focus on your technical skills, particularly in SQL, data structures, and data pipeline design, as well as your ability to communicate complex concepts clearly. Be prepared to demonstrate your problem-solving abilities and your experience with data analytics tools.

Data Structures and Algorithms

1. Can you explain the difference between a linked list and an array?

Understanding the fundamental differences between data structures is crucial for a Data Engineer role.

How to Answer

Discuss the characteristics of both data structures, including their memory allocation, access time, and use cases.

Example

“A linked list consists of nodes that contain data and a reference to the next node, allowing for dynamic memory allocation. In contrast, an array is a contiguous block of memory with fixed size, which allows for faster access times but can lead to wasted space if not fully utilized.”

2. How would you reverse a linked list?

This question tests your understanding of linked list manipulation.

How to Answer

Outline the steps you would take to reverse a linked list, emphasizing your approach to pointers.

Example

“To reverse a linked list, I would initialize three pointers: previous, current, and next. I would iterate through the list, adjusting the pointers so that each node points to its predecessor instead of its successor, effectively reversing the list.”

3. What is a hashmap, and how does it work?

This question assesses your knowledge of hashmaps, a common data structure.

How to Answer

Explain the concept of hashmaps, including how they store key-value pairs and the importance of hash functions.

Example

“A hashmap is a data structure that uses a hash function to map keys to values, allowing for average-case constant time complexity for lookups. The hash function converts the key into an index in an array, where the corresponding value is stored.”

4. Can you describe a binary tree and its traversal methods?

Understanding tree structures is essential for data manipulation tasks.

How to Answer

Discuss the structure of a binary tree and the different traversal methods, such as in-order, pre-order, and post-order.

Example

“A binary tree is a hierarchical structure where each node has at most two children. In-order traversal visits nodes in a left-root-right sequence, pre-order visits root-left-right, and post-order visits left-right-root.”

5. How would you implement a queue using stacks?

This question tests your problem-solving skills and understanding of data structures.

How to Answer

Explain the logic behind using two stacks to implement a queue and the operations involved.

Example

“To implement a queue using two stacks, I would use one stack for enqueue operations and another for dequeue operations. When dequeuing, if the second stack is empty, I would pop all elements from the first stack and push them onto the second stack, reversing their order.”

SQL and Data Manipulation

1. How do you optimize a SQL query?

This question assesses your SQL skills and understanding of performance tuning.

How to Answer

Discuss various techniques for optimizing SQL queries, such as indexing, avoiding SELECT *, and using joins effectively.

Example

“To optimize a SQL query, I would analyze the execution plan to identify bottlenecks, create indexes on frequently queried columns, and avoid using SELECT * to reduce the amount of data processed. Additionally, I would ensure that joins are performed on indexed columns to improve performance.”

2. Can you explain the concept of normalization in databases?

Understanding database design principles is crucial for a Data Engineer.

How to Answer

Define normalization and its purpose in database design, mentioning the different normal forms.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related tables and defining relationships between them. The first three normal forms are commonly used to achieve this.”

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

This question tests your advanced SQL knowledge.

How to Answer

Explain what window functions are and provide examples of their use cases.

Example

“Window functions perform calculations across a set of table rows related to the current row. They are useful for tasks like calculating running totals or ranking rows within a partition. For example, using ROW_NUMBER() can help assign a unique rank to each row within a specified partition.”

4. How do you handle NULL values in SQL?

This question assesses your understanding of data integrity and handling exceptions.

How to Answer

Discuss various methods for dealing with NULL values, including using COALESCE and ISNULL functions.

Example

“To handle NULL values in SQL, I would use the COALESCE function to return the first non-null value in a list. Additionally, I would ensure that my queries account for NULLs by using ISNULL or NULLIF to avoid unexpected results in calculations.”

5. Can you write a SQL query to find duplicate records in a table?

This question tests your practical SQL skills.

How to Answer

Outline the approach you would take to identify duplicates, including the use of GROUP BY and HAVING clauses.

Example

“To find duplicate records in a table, I would use a query like this: SELECT column_name, COUNT() FROM table_name GROUP BY column_name HAVING COUNT() > 1. This will return all records that appear more than once based on the specified column.”

Data Pipeline and ETL Processes

1. What is ETL, and how does it differ from ELT?

This question assesses your understanding of data processing methodologies.

How to Answer

Define ETL and ELT, highlighting their differences in data processing and storage.

Example

“ETL stands for Extract, Transform, Load, where data is extracted from source systems, transformed into a suitable format, and then loaded into a target system. ELT, on the other hand, extracts data and loads it into the target system first, allowing for transformation to occur within the target system, which is often more efficient for large datasets.”

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

This question tests your approach to maintaining data integrity.

How to Answer

Discuss the methods you use to validate and clean data throughout the pipeline.

Example

“To ensure data quality in my pipelines, I implement validation checks at each stage of the ETL process, such as schema validation, data type checks, and completeness checks. Additionally, I use logging and monitoring tools to track data quality metrics and address any issues promptly.”

3. Can you describe a time when you had to troubleshoot a data pipeline issue?

This question assesses your problem-solving skills in a real-world scenario.

How to Answer

Provide a specific example of a challenge you faced and how you resolved it.

Example

“I once encountered a data pipeline failure due to a schema change in the source database. I quickly identified the issue by reviewing the logs and implemented a temporary fix by adjusting the transformation logic. I then collaborated with the database team to ensure that future changes were communicated in advance to prevent similar issues.”

4. What tools do you use for data pipeline orchestration?

This question tests your familiarity with data engineering tools.

How to Answer

Discuss the tools you have experience with and their advantages.

Example

“I have experience using Apache Airflow for data pipeline orchestration due to its flexibility and ability to schedule complex workflows. Additionally, I have used tools like Apache NiFi for data flow management and AWS Glue for serverless ETL processes.”

5. How do you handle data versioning in your projects?

This question assesses your approach to managing changes in data.

How to Answer

Explain your strategies for tracking and managing data versions.

Example

“To handle data versioning, I implement a version control system for my datasets, where each version is tagged with a timestamp and a description of the changes made. This allows me to track changes over time and revert to previous versions if necessary, ensuring data integrity and reproducibility.”