Archer Daniels Midland Company Data Engineer Interview Questions + Guide in 2025

Archer Daniels Midland Company (ADM) is a global leader in human and animal nutrition, known for its innovative approach to agricultural origination and processing.

The Data Engineer role at ADM is crucial in designing, building, and maintaining robust data solutions that support strategic analytics initiatives. This position requires a collaborative mindset, as you'll engage with various functional teams to standardize data processes and enhance data quality across the organization. Key responsibilities include developing ETL/ELT pipelines, utilizing cloud technologies like Microsoft Azure, and adhering to DataOps and MLOps standards to optimize data delivery. A strong proficiency in programming languages such as Python and SQL, along with substantial experience in data warehousing concepts, is essential. The ideal candidate thrives in a fast-paced, agile environment, enjoys mentoring others, and is committed to continuous learning to stay ahead in the ever-evolving data landscape.

This guide is designed to help you prepare effectively for your interview at ADM by providing insights into the expectations and skills required for the Data Engineer role, giving you a competitive edge in the hiring process.

The interview process for a Data Engineer at Archer Daniels Midland Company is structured to assess both technical skills and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of your qualifications and experience.

1. Initial HR Screening

The first step in the interview process is an initial screening conducted by a recruiter. This is usually a 30- to 45-minute phone call where the recruiter will discuss your background, the role, and the company culture. They will assess your interest in the position and determine if your skills align with the requirements of the Data Engineer role. This is also an opportunity for you to ask questions about the company and the team.

2. Technical Interview

Following the HR screening, candidates typically undergo a technical interview. This round may involve one or two data engineers and lasts about an hour. During this interview, you can expect to discuss your experience with data warehousing concepts, ETL processes, and specific technologies such as Microsoft Azure, SQL, and Python. You may also be asked to solve technical problems or case studies that demonstrate your analytical and problem-solving skills.

3. Behavioral Interview

After the technical interview, candidates often participate in a behavioral interview. This round focuses on assessing your soft skills, teamwork, and how you handle challenges in a collaborative environment. Interviewers will ask about past experiences, how you approach problem-solving, and your ability to mentor and lead others. This is crucial for understanding how you would fit into the ADM culture and work with cross-functional teams.

4. Final Interview

The final interview may involve meeting with senior management or team leads. This round is typically more conversational and aims to gauge your long-term fit within the company. You may discuss your career aspirations, how you can contribute to ADM's goals, and your understanding of the industry. This is also a chance for you to showcase your enthusiasm for the role and the company.

5. Offer and Background Check

If you successfully navigate the interview rounds, you will receive a job offer. The final step involves a background check, which is standard for all candidates. This ensures that all information provided during the interview process is verified.

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

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

Understand Data Engineering Fundamentals

Before your interview, ensure you have a solid grasp of data engineering concepts, particularly those relevant to the role at ADM. Familiarize yourself with data warehousing principles, including star and snowflake schemas, slowly changing dimensions (SCD), and the differences between fact and dimension tables. This foundational knowledge will help you answer questions confidently and demonstrate your expertise.

Prepare for Technical Questions

Given the emphasis on technical skills in the role, be ready to discuss your experience with Microsoft Azure, SQL, and data pipeline development. Brush up on your knowledge of Azure Data Factory, Azure Synapse, and Databricks, as these are critical tools for the position. Practice articulating your past projects, focusing on the challenges you faced and how you overcame them, particularly in the context of data integration and ETL processes.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with hypothetical scenarios or problems to solve. Approach these questions methodically: clarify the problem, outline your thought process, and explain how you would implement a solution. Emphasize your analytical skills and your ability to collaborate with team members to tackle complex challenges, as teamwork is highly valued at ADM.

Emphasize Continuous Learning

ADM values candidates who are committed to continuous learning and improvement. Be prepared to discuss how you stay updated with the latest trends and technologies in data engineering. Mention any relevant courses, certifications, or personal projects that demonstrate your initiative to enhance your skills and knowledge.

Communicate Effectively

Strong communication skills are essential for a Data Engineer at ADM, as you will need to liaise with various stakeholders. Practice explaining technical concepts in a clear and concise manner, ensuring that you can adapt your communication style to suit different audiences. Highlight any experience you have in presenting ideas or leading discussions, as this will showcase your ability to influence and collaborate effectively.

Align with Company Culture

ADM places a strong emphasis on diversity, equity, and inclusion. Familiarize yourself with the company's values and initiatives in this area, and be prepared to discuss how you can contribute to a positive and inclusive work environment. Show your enthusiasm for being part of a team that values diverse perspectives and fosters collaboration.

Be Yourself

Finally, while it's important to prepare and present your best self, don't forget to be authentic. The interviewers at ADM are looking for candidates who are not only technically proficient but also a good cultural fit. Share your passion for data engineering and how it aligns with ADM's mission to provide access to nutrition worldwide. Your genuine interest and enthusiasm can set you apart from other candidates.

By following these tips, you will be well-prepared to make a strong impression during your interview for the Data Engineer role at Archer Daniels Midland Company. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Archer Daniels Midland Company. The interview will focus on your technical skills, problem-solving abilities, and experience with data engineering concepts, particularly in relation to data pipelines, data warehousing, and cloud technologies. Be prepared to discuss your past experiences and how they relate to the responsibilities outlined in the job description.

Data Engineering Concepts

1. Can you explain the difference between ETL and ELT?

Understanding the distinction between these two processes is crucial for a Data Engineer, especially in a cloud-based environment.

How to Answer

Discuss the processes involved in ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), emphasizing the scenarios in which each is used.

Example

“ETL involves extracting data from various sources, transforming it into a suitable format, and then loading it into a data warehouse. In contrast, ELT extracts data and loads it into the data warehouse first, allowing for transformation to occur within the warehouse itself. This is particularly useful in cloud environments where storage is scalable and processing power can be leveraged for transformations.”

2. What are slowly changing dimensions (SCD) and how do you handle them?

This question assesses your knowledge of data warehousing and how to manage historical data.

How to Answer

Explain the different types of SCDs (Type 1, Type 2, Type 3) and provide examples of when you would use each type.

Example

“Slowly changing dimensions are used to manage and track changes in dimension data over time. For instance, Type 1 overwrites old data with new data, while Type 2 creates a new record for each change, preserving historical data. I typically use Type 2 for customer data to maintain a complete history of customer interactions.”

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

Data quality is critical for effective analytics and decision-making.

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools or frameworks you employ.

Example

“I ensure data quality by implementing validation checks at various stages of the pipeline, such as schema validation and data type checks. Additionally, I use tools like Azure Data Factory to automate data cleansing processes, ensuring that only high-quality data is loaded into our systems.”

4. Describe your experience with data warehousing solutions.

This question gauges your familiarity with data warehousing technologies and methodologies.

How to Answer

Talk about specific data warehousing solutions you have worked with, including any relevant projects.

Example

“I have extensive experience with Azure Synapse and SQL Server for data warehousing. In my previous role, I designed a data warehouse that integrated data from multiple sources, allowing for comprehensive reporting and analytics. I focused on optimizing the schema for performance and ensuring that the data was easily accessible for end-users.”

5. What strategies do you use for optimizing data pipelines?

Optimization is key to ensuring efficient data processing.

How to Answer

Discuss specific techniques you use to improve the performance of data pipelines.

Example

“I optimize data pipelines by implementing parallel processing and partitioning strategies to reduce processing time. Additionally, I regularly monitor performance metrics and adjust resource allocation in Azure to ensure that the pipelines run efficiently, especially during peak loads.”

Programming and Tools

1. What programming languages are you proficient in, and how have you used them in data engineering?

This question assesses your technical skills and experience with relevant programming languages.

How to Answer

List the programming languages you are familiar with and provide examples of how you have applied them in your work.

Example

“I am proficient in Python and SQL, which I use extensively for data manipulation and ETL processes. For instance, I developed a Python script that automated data extraction from APIs and transformed the data for loading into our data warehouse, significantly reducing manual effort.”

2. Can you explain how you have used Azure Data Factory in your projects?

This question evaluates your hands-on experience with Azure Data Factory.

How to Answer

Provide specific examples of how you have utilized Azure Data Factory for data integration and pipeline creation.

Example

“I have used Azure Data Factory to orchestrate data movement between various sources and our data warehouse. I created pipelines that automate the extraction of data from on-premises SQL databases and load it into Azure Synapse, ensuring that our data is always up-to-date for reporting purposes.”

3. How do you handle version control in your data engineering projects?

Version control is essential for collaboration and maintaining code integrity.

How to Answer

Discuss the tools you use for version control and how you implement best practices.

Example

“I use Git for version control in my data engineering projects. I follow best practices by creating branches for new features and regularly merging changes to the main branch after thorough testing. This approach helps maintain code quality and facilitates collaboration with my team.”

4. Describe your experience with CI/CD in data engineering.

Continuous Integration and Continuous Deployment (CI/CD) practices are important for efficient development.

How to Answer

Explain how you have implemented CI/CD pipelines in your data engineering work.

Example

“I have implemented CI/CD pipelines using Azure DevOps to automate the deployment of data pipelines. This includes automated testing of code changes and deployment to production environments, which has significantly reduced the time it takes to deliver new features and fixes.”

5. What tools do you use for data visualization, and how do you integrate them with your data pipelines?

This question assesses your ability to present data effectively.

How to Answer

Discuss the data visualization tools you are familiar with and how you connect them to your data sources.

Example

“I primarily use Power BI for data visualization. I integrate it with our data pipelines by connecting Power BI directly to our Azure Synapse data warehouse, allowing for real-time reporting and dashboards that provide insights to stakeholders.”