Forge Data Engineer Interview Questions + Guide in 2025

Forge is a forward-thinking global consulting firm that redefines the traditional government contracting landscape, focusing on collective impact and community-based solutions.

As a Data Engineer at Forge, you will play a pivotal role in supporting data-driven projects primarily for the U.S. Army. Your key responsibilities will include developing data transformation microservices, creating robust data ingress and egress pipelines using APIs and SFTP methods, and migrating historical data to modern formats. This role demands proficiency in AWS and Python, as well as the ability to maintain detailed documentation of interface definitions. A strong candidate will possess a strategic mindset, exceptional analytical skills, and the ability to thrive in a dynamic, entrepreneurial environment.

Forge values cross-group collaboration and encourages employees to harness their unique strengths, making effective communication and relationship-building essential traits for success in this role. With a commitment to employee well-being and growth, this position aligns with Forge’s mission to cultivate a diverse and inclusive workplace where innovation can flourish.

This guide will equip you with the insights needed to navigate your interview effectively, helping you to articulate your skills and experiences in a way that resonates with Forge's core values and mission.

The interview process for a Data Engineer position at Forge is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a thorough evaluation that emphasizes collaboration, problem-solving, and technical expertise.

1. Initial Screening

The process begins with an initial screening, typically conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on understanding the candidate's background, skills, and motivations for applying to Forge. The recruiter will also provide insights into the company culture and the specific expectations for the Data Engineer role.

2. Coding Challenge

Following the initial screening, candidates are usually required to complete a coding challenge. This challenge assesses the candidate's proficiency in data structures and algorithms, as well as their ability to write clean, efficient code. The challenge may involve tasks related to data manipulation, pipeline creation, or API development, reflecting the technical requirements of the role.

3. Technical Interview

Candidates who successfully complete the coding challenge will move on to a technical interview. This interview is typically conducted via video conferencing and focuses on the candidate's technical knowledge and problem-solving abilities. Interviewers may ask questions related to AWS, Python, data pipelines, and API development, as well as scenarios that require the candidate to demonstrate their analytical thinking and strategic problem-solving skills.

4. Onsite Interview

The final stage of the interview process is an onsite interview, which consists of multiple rounds—often five one-on-one interviews with various team members. These interviews cover a range of topics, including technical skills, behavioral questions, and situational assessments. Candidates should be prepared to discuss their past experiences, how they approach challenges, and their ability to work collaboratively within a team. The onsite interviews also provide an opportunity for candidates to learn more about Forge's culture and values.

As you prepare for your interview, consider the types of questions that may arise during these stages.

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

Embrace the Collaborative Culture

Forge prides itself on a culture that emphasizes collaboration and community. During your interview, showcase your ability to work well in teams and your experience in collaborative projects. Be prepared to discuss specific examples where you contributed to a team effort, especially in problem-solving scenarios. Highlight how you value diverse perspectives and how you can bring unique insights to the table.

Prepare for a Multi-Round Interview Process

Expect a thorough interview process that includes multiple rounds, such as coding challenges and technical interviews. Familiarize yourself with the types of questions that may arise, particularly those related to data structures, algorithms, and your experience with AWS and Python. Practice coding problems and be ready to explain your thought process clearly and concisely. Remember, the interviewers are looking for not just the right answers, but also how you approach problem-solving.

Showcase Your Technical Proficiency

As a Data Engineer, you will be expected to demonstrate a strong command of technical skills. Brush up on your knowledge of data pipelines, APIs, and microservices. Be prepared to discuss your experience with AWS, Python, and any relevant tools like Jira and GitLab. If you have experience with security clearances or certifications, such as AWS or CompTIA Security+, make sure to mention these as they are highly valued.

Communicate Clearly and Effectively

Forge values excellent communication skills, both written and verbal. During the interview, articulate your thoughts clearly and ensure you can explain complex technical concepts in a way that is understandable. Practice explaining your past projects and the impact they had on your team or organization. This will demonstrate your ability to communicate effectively with both technical and non-technical stakeholders.

Demonstrate Strategic and Critical Thinking

The role requires a candidate who can think strategically and act tactically. Be prepared to discuss how you approach problem-solving and decision-making. Share examples of how you have identified challenges, gathered data, and developed solutions in previous roles. Highlight your ability to adapt to changing conditions and how you prioritize tasks in a fast-paced environment.

Align with Forge’s Values

Forge is committed to putting people first and fostering a diverse and inclusive workplace. Familiarize yourself with the company’s values and mission, and be ready to discuss how your personal values align with theirs. Show your enthusiasm for being part of a company that prioritizes employee well-being and community impact.

Be Yourself and Enjoy the Process

Lastly, remember that the interview is as much about you assessing Forge as it is about them assessing you. Be authentic and let your personality shine through. Forge’s interviewers are known for being welcoming and friendly, so take the opportunity to engage with them and enjoy the conversation. This will help you gauge if Forge is the right fit for you as well.

By following these tips, you will be well-prepared to make a strong impression during your interview at Forge. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Forge. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to demonstrate your knowledge of data engineering concepts, tools, and methodologies, as well as your ability to work collaboratively in a team-oriented environment.

Technical Skills

1. Can you explain the process of building a data pipeline from scratch?

This question assesses your understanding of data pipeline architecture and your practical experience in building them.

How to Answer

Outline the steps involved in designing and implementing a data pipeline, including data ingestion, transformation, and storage. Mention any specific tools or technologies you have used in the past.

Example

“To build a data pipeline, I start by identifying the data sources and determining the best method for ingestion, whether through APIs or batch processing. Next, I design the transformation logic to clean and format the data, using tools like AWS Glue or Apache Spark. Finally, I store the processed data in a suitable database, such as Amazon Redshift, ensuring that it is accessible for analysis.”

2. What experience do you have with AWS services relevant to data engineering?

This question evaluates your familiarity with cloud technologies, particularly AWS, which is crucial for the role.

How to Answer

Discuss specific AWS services you have used, such as S3, Lambda, or Redshift, and how they fit into your data engineering projects.

Example

“I have extensive experience using AWS S3 for data storage and retrieval, and I often utilize AWS Lambda for serverless data processing tasks. Additionally, I have worked with Amazon Redshift for data warehousing, where I implemented ETL processes to migrate data from various sources into the warehouse for analysis.”

3. Describe a challenging data transformation project you worked on. What were the challenges, and how did you overcome them?

This question aims to understand your problem-solving skills and ability to handle complex data scenarios.

How to Answer

Provide a specific example of a project, detailing the challenges faced and the strategies you employed to resolve them.

Example

“In a recent project, I was tasked with migrating historical data from a legacy system to a new format. The challenge was ensuring data integrity during the transformation. I implemented a series of validation checks and created a rollback plan to address any discrepancies. This approach allowed us to successfully migrate the data without any loss.”

Data Management

4. How do you ensure data quality and integrity in your pipelines?

This question assesses your understanding of data governance and quality assurance practices.

How to Answer

Discuss the methods and tools you use to monitor and maintain data quality throughout the data lifecycle.

Example

“I ensure data quality by implementing validation rules at various stages of the pipeline. I use tools like Apache Airflow to schedule and monitor data workflows, and I regularly conduct data audits to identify and rectify any anomalies. Additionally, I maintain comprehensive documentation to track data lineage and transformations.”

5. What strategies do you use for optimizing data storage and retrieval?

This question evaluates your knowledge of data storage solutions and performance optimization techniques.

How to Answer

Explain the techniques you use to optimize data storage, such as partitioning, indexing, or using appropriate data formats.

Example

“I optimize data storage by using partitioning strategies in databases to improve query performance. I also leverage columnar storage formats like Parquet for analytical workloads, which significantly reduces storage costs and speeds up data retrieval times. Additionally, I regularly analyze query performance and adjust indexing strategies as needed.”

Collaboration and Communication

6. How do you approach working with cross-functional teams, such as data scientists and analysts?

This question assesses your ability to collaborate and communicate effectively with different stakeholders.

How to Answer

Discuss your experience working with various teams and how you ensure alignment on project goals.

Example

“I prioritize open communication and regular check-ins with cross-functional teams. In my previous role, I organized weekly meetings to discuss project progress and gather feedback from data scientists and analysts. This collaborative approach helped us align on objectives and ensured that the data solutions I developed met their analytical needs.”

7. Can you provide an example of how you communicated a complex technical concept to a non-technical audience?

This question evaluates your ability to simplify complex information for diverse audiences.

How to Answer

Share a specific instance where you successfully explained a technical concept to a non-technical stakeholder.

Example

“During a project presentation, I needed to explain the data pipeline architecture to a group of executives. I used visual aids and analogies to illustrate the flow of data and its impact on business decisions. By focusing on the benefits rather than the technical details, I was able to engage the audience and ensure they understood the value of our work.”