Primus Data Engineer Interview Questions + Guide in 2025

Primus is a leading provider of technology solutions, specializing in data architecture and engineering to optimize business processes and decision-making.

As a Data Engineer at Primus, you will be responsible for designing, building, and maintaining scalable data pipelines that handle large volumes of data across various platforms. The role requires hands-on experience with big data frameworks such as Hadoop and Spark, as well as proficiency in SQL for data manipulation. You will also work with cloud-based services like AWS, ensuring data security, governance, and compliance with organizational standards. A solid foundation in data architecture principles and experience with data visualization tools may also be advantageous. Strong communication skills are vital, as you will collaborate with cross-functional teams to translate business requirements into technical specifications.

This guide will help you prepare for your job interview at Primus by providing insight into the role and the skills required, ultimately giving you a competitive edge.

The interview process for a Data Engineer role at Primus is structured to assess both technical skills and cultural fit. Candidates can expect a multi-step process that evaluates their expertise in data engineering, problem-solving abilities, and communication skills.

1. Initial Screening

The first step in the interview process is an initial screening conducted by a recruiter. This typically lasts about 30 minutes and focuses on understanding the candidate's background, experience, and motivations for applying to Primus. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that candidates have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via a video call. This assessment is designed to evaluate the candidate's proficiency in key technical areas relevant to the role, such as SQL, Python, and data pipeline development using tools like Hadoop and Spark. Candidates should be prepared to solve coding problems, discuss their previous projects, and demonstrate their understanding of data engineering concepts.

3. Behavioral Interview

After successfully passing the technical assessment, candidates will participate in a behavioral interview. This round typically involves one or more interviewers and focuses on assessing the candidate's soft skills, teamwork, and problem-solving approach. Candidates should be ready to discuss past experiences, how they handle challenges, and their ability to collaborate with cross-functional teams.

4. Onsite Interview (or Final Round)

The final stage of the interview process may involve an onsite interview or a final round of video interviews. This stage usually consists of multiple one-on-one interviews with team members and stakeholders. Candidates can expect to dive deeper into technical discussions, including data architecture, ETL processes, and cloud technologies. Additionally, this round may include situational questions to gauge how candidates would approach real-world challenges they might face in the role.

5. Offer and Negotiation

If a candidate successfully navigates the previous rounds, they will receive a job offer. This stage may involve discussions about salary, benefits, and other employment terms. Candidates should be prepared to negotiate based on their experience and the market standards for Data Engineers.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during each stage of the process.

Primus Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Primus. The interview will assess your technical skills in data engineering, including your proficiency in SQL, data pipeline design, and experience with big data technologies. Be prepared to discuss your past experiences and how they relate to the responsibilities of the role.

Technical Skills

1. Can you explain the differences between OLAP and OLTP systems?

Understanding the distinctions between these two types of systems is crucial for data engineers, as they impact how data is structured and accessed.

How to Answer

Discuss the characteristics of OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) systems, focusing on their purposes, data structures, and typical use cases.

Example

“OLAP systems are designed for complex queries and data analysis, often involving large volumes of historical data, while OLTP systems are optimized for transaction processing and real-time data entry. For instance, a data warehouse would typically use OLAP for reporting, whereas an e-commerce application would rely on OLTP for managing customer transactions.”

2. Describe your experience with ETL processes. What tools have you used?

ETL (Extract, Transform, Load) processes are fundamental to data engineering, and familiarity with various tools is essential.

How to Answer

Highlight your experience with specific ETL tools and frameworks, explaining how you have implemented ETL processes in past projects.

Example

“I have extensive experience with Apache NiFi and AWS Glue for ETL processes. In my previous role, I designed a data pipeline using NiFi to extract data from multiple sources, transform it for analysis, and load it into a Redshift data warehouse, ensuring data quality and integrity throughout the process.”

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

Data quality is critical for reliable analytics and reporting, and interviewers will want to know your approach to maintaining it.

How to Answer

Discuss the strategies and tools you use to monitor and validate data quality, including any automated testing or validation processes.

Example

“I implement data validation checks at various stages of the ETL process, such as schema validation and data type checks. Additionally, I use tools like Great Expectations to automate data quality testing, ensuring that any anomalies are flagged before the data is loaded into the warehouse.”

4. What is your experience with big data technologies like Hadoop and Spark?

Big data technologies are often used in data engineering roles, and familiarity with them is a plus.

How to Answer

Share your hands-on experience with Hadoop and Spark, including specific projects where you utilized these technologies.

Example

“I have over three years of experience working with Hadoop and Spark. In my last project, I used Spark to process large datasets for real-time analytics, leveraging its in-memory processing capabilities to improve performance significantly compared to traditional Hadoop MapReduce jobs.”

5. Can you explain how you would design a data pipeline for a new application?

This question assesses your ability to architect data solutions from scratch.

How to Answer

Outline the steps you would take to design a data pipeline, including data sources, transformation processes, and storage solutions.

Example

“To design a data pipeline for a new application, I would first identify the data sources and the types of data to be ingested. Next, I would define the transformation logic required to clean and enrich the data. I would then choose an appropriate storage solution, such as AWS S3 for raw data and Redshift for processed data, and finally, I would implement monitoring and logging to ensure the pipeline runs smoothly.”

SQL and Data Manipulation

1. How do you optimize SQL queries for performance?

Optimizing SQL queries is essential for efficient data retrieval, and interviewers will want to know your strategies.

How to Answer

Discuss techniques you use to improve SQL query performance, such as indexing, query restructuring, and analyzing execution plans.

Example

“I optimize SQL queries by analyzing execution plans to identify bottlenecks. I often use indexing on frequently queried columns and rewrite complex joins into simpler subqueries when possible. For instance, in a recent project, I reduced query execution time by 50% by adding indexes and restructuring the query logic.”

2. Describe a complex SQL query you have written. What was its purpose?

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

How to Answer

Provide a specific example of a complex SQL query, explaining its purpose and the challenges you faced.

Example

“I wrote a complex SQL query to generate a sales report that aggregated data from multiple tables, including sales, customers, and products. The query involved several joins and subqueries to calculate total sales by region and product category. I faced challenges with performance, which I resolved by optimizing the joins and using temporary tables to store intermediate results.”

3. What are window functions in SQL, and how have you used them?

Window functions are powerful tools for data analysis, and familiarity with them is important for data engineers.

How to Answer

Explain what window functions are and provide examples of how you have used them in your work.

Example

“Window functions allow you to perform calculations across a set of rows related to the current row. I have used them to calculate running totals and moving averages in sales data analysis. For example, I used the SUM() window function to calculate the cumulative sales for each month, which helped the business identify trends over time.”

4. How do you handle missing or inconsistent data in your datasets?

Data quality issues are common, and interviewers will want to know your approach to handling them.

How to Answer

Discuss the methods you use to identify and address missing or inconsistent data, including any tools or techniques.

Example

“I handle missing data by first identifying the extent of the issue using data profiling techniques. Depending on the situation, I may choose to impute missing values using statistical methods or remove records with excessive missing data. For instance, in a recent project, I used mean imputation for numerical fields while flagging records for further review.”

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

Understanding these concepts is crucial for effective database design.

How to Answer

Define normalization and denormalization, explaining their purposes and when to use each approach.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity, typically through the use of multiple related tables. Denormalization, on the other hand, involves combining tables to improve read performance at the cost of increased redundancy. I often normalize data during the design phase but may denormalize for reporting purposes to enhance query performance.”