PNC Data Engineer Interview Questions + Guide in 2025

PNC is a prominent financial services institution committed to delivering exceptional customer experiences through an inclusive and collaborative workplace culture.

As a Data Engineer at PNC, your primary responsibility will be to develop, support, and implement data services across various applications, ensuring alignment with business objectives. You will work closely with users, developers, and business partners to understand data service requirements and design scalable data infrastructures on multiple platforms. Proficiency in big data technologies such as Hadoop, Azure, and Kafka, along with hands-on experience in automating processes using scripting languages like Python and PowerShell, will be crucial to your success. You will also be expected to optimize existing data processes for reliability and performance, monitor enterprise data availability, and contribute to incident resolution and root cause analysis.

The ideal candidate will possess strong analytical skills and a solid foundation in SQL for database management, along with the ability to communicate effectively with cross-functional teams. PNC values customer focus and risk management, so demonstrating an understanding of customer needs and the capacity to manage risks will be essential during the interview process.

This guide will help you prepare thoroughly for your interview by highlighting the specific skills and attributes PNC seeks in a Data Engineer, enabling you to present your qualifications effectively.

The interview process for a Data Engineer position at PNC is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, each designed to evaluate different competencies relevant to the role.

1. Initial Phone Screen

The process typically begins with a 30-minute phone interview conducted by a recruiter. This initial screen focuses on understanding the candidate's background, skills, and motivations for applying to PNC. The recruiter will also provide insights into the company culture and the specific expectations for the Data Engineer role. Candidates should be prepared to discuss their experience with big data technologies, scripting languages, and any relevant projects they have worked on.

2. Technical Interview

Following the initial screen, candidates will participate in a technical interview, which may be conducted via video conferencing. This interview is usually led by a senior data engineer or a technical manager. During this session, candidates can expect to tackle questions related to data architecture, ETL processes, and big data tools such as Hadoop, Kafka, and Azure. Additionally, candidates may be asked to solve coding problems or demonstrate their ability to optimize SQL queries, as well as discuss their experience with data monitoring and business intelligence solutions.

3. Onsite Interview

The final stage of the interview process is an onsite interview, which typically consists of multiple rounds with various team members, including data engineers, analysts, and managers. Each interview lasts approximately 45 minutes and covers a mix of technical and behavioral questions. Candidates will be evaluated on their problem-solving abilities, communication skills, and how well they collaborate with cross-functional teams. This stage may also include a practical assessment where candidates demonstrate their technical skills through real-world scenarios or case studies.

4. Final Discussion

After the onsite interviews, candidates may have a final discussion with a hiring manager or team lead. This conversation often focuses on the candidate's fit within the team and the organization, as well as discussing potential career growth opportunities at PNC. Candidates should be ready to articulate their long-term career goals and how they align with PNC's mission and values.

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.

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

Understand the Company Culture

PNC emphasizes a collaborative and inclusive workplace where employees feel respected and valued. Familiarize yourself with PNC's core values and how they align with your own. Be prepared to discuss how you can contribute to fostering this culture, particularly in a data engineering context where teamwork and communication are crucial.

Prepare for Technical Proficiency

Given the technical nature of the Data Engineer role, ensure you are well-versed in big data technologies such as Hadoop, Azure, and Kafka. Brush up on your scripting skills in languages like Python and PowerShell, and be ready to demonstrate your ability to automate processes. Familiarity with ETL tools and advanced SQL queries will also be essential, so practice these skills thoroughly.

Showcase Problem-Solving Skills

During the interview, be prepared to discuss specific examples of how you've tackled complex data challenges in the past. PNC values analytical thinking and problem-solving abilities, so highlight instances where your interventions led to improved data processes or solutions. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Communicate Effectively

Strong communication skills are vital for this role, as you will need to collaborate with various stakeholders, including business leaders and analysts. Practice articulating your thoughts clearly and concisely. Be ready to explain technical concepts in a way that non-technical team members can understand, demonstrating your ability to bridge the gap between technical and business perspectives.

Be Ready for Behavioral Questions

Expect behavioral interview questions that assess your fit within PNC's culture. Prepare to discuss how you manage risk, prioritize customer needs, and work in a team environment. Reflect on past experiences that showcase your adaptability, resilience, and commitment to delivering high-quality results.

Follow Up Professionally

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your enthusiasm for the role and briefly mention how your skills align with PNC's goals. This not only shows professionalism but also reinforces your interest in the position.

By following these tips, you can present yourself as a strong candidate who is not only technically proficient but also a great cultural fit for PNC. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at PNC. The interview will likely focus on your technical skills, problem-solving abilities, and experience with data management and engineering practices. Be prepared to discuss your familiarity with big data technologies, ETL processes, and your approach to optimizing data solutions.

Technical Skills

1. Can you explain the differences between Hadoop and Spark, and when you would use one over the other?

Understanding the strengths and weaknesses of these big data technologies is crucial for a Data Engineer role.

How to Answer

Discuss the architecture of both frameworks, their processing capabilities, and specific use cases where one might be preferred over the other.

Example

"Hadoop is great for batch processing and is cost-effective for storing large datasets, while Spark excels in real-time data processing and in-memory computations. I would choose Spark for applications requiring low latency and quick data retrieval, such as real-time analytics, while Hadoop would be suitable for large-scale data storage and batch processing tasks."

2. Describe your experience with ETL tools. Which tools have you used, and what was your role in the ETL process?

This question assesses your hands-on experience with data extraction, transformation, and loading.

How to Answer

Mention specific ETL tools you have used, your responsibilities in the ETL process, and any challenges you faced.

Example

"I have worked extensively with Informatica and Talend for ETL processes. My role involved designing data pipelines, transforming data to meet business requirements, and ensuring data quality. One challenge I faced was optimizing the data load times, which I addressed by implementing incremental loading strategies."

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

Data quality is critical in any data-driven organization, and PNC will want to know your approach.

How to Answer

Discuss the methods you use to validate data, monitor data quality, and handle discrepancies.

Example

"I implement data validation checks at various stages of the ETL process, such as schema validation and data profiling. Additionally, I set up monitoring alerts for data anomalies and regularly conduct audits to ensure data integrity."

4. Can you explain how you would optimize a slow-running SQL query?

This question tests your SQL skills and understanding of database performance.

How to Answer

Discuss techniques such as indexing, query rewriting, and analyzing execution plans.

Example

"I would start by analyzing the execution plan to identify bottlenecks. If the query is scanning large tables, I would consider adding appropriate indexes. Additionally, I would look for opportunities to rewrite the query to reduce complexity and improve performance."

5. What is your experience with cloud platforms, particularly AWS?

As PNC utilizes cloud technologies, your familiarity with AWS will be important.

How to Answer

Mention specific AWS services you have used and how they relate to data engineering tasks.

Example

"I have experience using AWS services like S3 for data storage, Redshift for data warehousing, and Glue for ETL processes. I recently migrated a legacy data pipeline to AWS, which improved scalability and reduced costs significantly."

Problem-Solving and Analytical Skills

1. Describe a challenging data engineering problem you faced and how you resolved it.

This question assesses your problem-solving skills and ability to handle complex situations.

How to Answer

Provide a specific example, detailing the problem, your approach, and the outcome.

Example

"I encountered a situation where our data ingestion process was failing due to schema changes in the source system. I quickly collaborated with the source team to understand the changes and updated our ETL mappings accordingly. I also implemented a versioning system for our schemas to prevent similar issues in the future."

2. How do you approach data modeling for a new project?

This question evaluates your understanding of data architecture and modeling principles.

How to Answer

Discuss your process for gathering requirements, designing models, and ensuring scalability.

Example

"I start by gathering requirements from stakeholders to understand their data needs. Then, I create an initial conceptual model, followed by a logical model that defines the relationships between entities. Finally, I translate this into a physical model, ensuring it is optimized for performance and scalability."

3. What strategies do you use for monitoring and maintaining data pipelines?

This question focuses on your ability to ensure the reliability of data processes.

How to Answer

Discuss the tools and techniques you use for monitoring and alerting.

Example

"I use tools like Apache Airflow for scheduling and monitoring data pipelines. I set up alerts for failures and performance issues, and I regularly review logs to identify potential problems before they escalate."

4. How do you handle data privacy and compliance in your projects?

Given the financial nature of PNC, compliance is critical.

How to Answer

Discuss your understanding of data privacy regulations and how you implement compliance measures.

Example

"I ensure compliance with regulations like GDPR by implementing data anonymization techniques and access controls. I also conduct regular audits to ensure that our data handling practices align with legal requirements."

5. Can you explain the concept of data lineage and its importance?

Understanding data lineage is essential for data governance and compliance.

How to Answer

Discuss what data lineage is and why it matters in data engineering.

Example

"Data lineage refers to the tracking of data from its origin through its lifecycle to its final destination. It is crucial for understanding data transformations, ensuring data quality, and meeting compliance requirements. I implement data lineage tracking in our ETL processes to provide transparency and facilitate audits."

Communication and Collaboration

1. How do you communicate complex technical concepts to non-technical stakeholders?

This question assesses your communication skills.

How to Answer

Discuss your approach to simplifying technical jargon and using visual aids.

Example

"I focus on using simple language and analogies to explain complex concepts. I also use visual aids like diagrams and flowcharts to illustrate processes, which helps non-technical stakeholders grasp the information more easily."

2. Describe a time when you had to collaborate with cross-functional teams. How did you ensure effective communication?

Collaboration is key in a data engineering role, and PNC will want to know how you handle it.

How to Answer

Provide an example of a project where you worked with different teams and how you facilitated communication.

Example

"During a recent project, I collaborated with data analysts and business stakeholders to define data requirements. I scheduled regular check-ins and used collaborative tools like Slack and Confluence to keep everyone updated on progress and gather feedback."

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

This question evaluates your time management skills.

How to Answer

Discuss your approach to prioritization and how you manage deadlines.

Example

"I prioritize tasks based on their impact on business objectives and deadlines. I use project management tools to track progress and ensure that I allocate time effectively to meet all project requirements."

4. How do you handle conflicts within a team?

Conflict resolution is important for maintaining a productive work environment.

How to Answer

Discuss your approach to addressing conflicts and fostering collaboration.

Example

"I believe in addressing conflicts directly and openly. I encourage team members to express their concerns and facilitate discussions to find common ground. My goal is to ensure that everyone feels heard and that we can work together towards a solution."

5. What role do you think a Data Engineer plays in supporting business objectives?

This question assesses your understanding of the strategic importance of data engineering.

How to Answer

Discuss how data engineering contributes to data-driven decision-making and business success.

Example

"A Data Engineer plays a crucial role in enabling data-driven decision-making by ensuring that high-quality data is available for analysis. By building efficient data pipelines and optimizing data storage, we empower business teams to derive insights that drive strategic initiatives."