Te Connectivity Data Engineer Interview Questions + Guide in 2025

TE Connectivity is a global industrial technology leader committed to creating a safer, sustainable, productive, and connected future through innovative connectivity and sensor solutions.

As a Data Engineer at TE Connectivity, you will be responsible for designing and establishing solution architectures for analytical platforms and data-enabled solutions. You will have a comprehensive understanding of the data and analytics ecosystem, working hands-on in data engineering, data science, and data visualization. Your key responsibilities will include creating robust data pipelines, optimizing ETL processes, and ensuring high-quality data integrity across various systems. Proficiency in PL/SQL, complex SQL tuning, and experience with cloud databases, particularly AWS, will be essential. You will also engage in developing service-oriented architectures and RESTful APIs while collaborating with cross-functional teams to drive project success under agile methodologies. Strong problem-solving skills, attention to detail, and effective communication will make you a great fit for this role, aligning with TE Connectivity's values of integrity, accountability, and teamwork.

This guide will help you prepare for your interview by providing insights into the essential skills and experiences that TE Connectivity seeks in a Data Engineer, enabling you to present your qualifications confidently and effectively.

The interview process for a Data Engineer position at Te Connectivity is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Screening

The first step in the interview process is an initial screening, which is usually conducted over the phone. This 30-minute conversation is led by a recruiter who will evaluate your basic qualifications and experience relevant to the Data Engineer role. During this call, you will discuss your background, technical skills, and understanding of the data engineering landscape, as well as your motivation for applying to Te Connectivity.

2. Technical Interview

Following the initial screening, candidates will participate in a technical interview, also conducted over the phone. This interview lasts approximately 30 minutes and focuses on your proficiency in key technical areas such as AWS, Python, SQL, and ETL processes. You may be asked to solve problems or discuss your past projects to demonstrate your technical capabilities and problem-solving skills.

3. Onsite Interview

The onsite interview is the final stage of the process and typically consists of multiple rounds of interviews with various team members. Each round lasts around 45 minutes and covers a mix of technical and behavioral questions. You will be assessed on your knowledge of data architecture, data pipeline development, and your ability to work with big data technologies. Additionally, expect discussions around your experience with service-oriented architecture (SOA) and your approach to integrating data solutions within a business context.

Throughout the onsite interviews, you will also have the opportunity to showcase your interpersonal skills and how you collaborate with cross-functional teams, as these are essential for success in the role.

As you prepare for the interview, it’s important to familiarize yourself with the types of questions that may be asked during this process.

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

Understand the Technical Landscape

As a Data Engineer at TE Connectivity, you will be expected to have a broad understanding of the data and analytics ecosystem. Familiarize yourself with the specific tools and technologies mentioned in the job description, such as AWS, PL/SQL, and ETL processes. Be prepared to discuss your hands-on experience with these technologies and how you have applied them in previous roles. Highlight any projects where you designed or optimized data pipelines, as this will demonstrate your capability to contribute to the team’s development velocity.

Prepare for Technical Assessments

Expect a technical interview that may include coding challenges or problem-solving scenarios. Brush up on your Python skills, as well as your knowledge of SQL, particularly complex queries and performance tuning. Practice articulating your thought process while solving problems, as interviewers will be looking for your approach to data engineering challenges. Be ready to explain your experience with data warehousing best practices and how you ensure data quality and reliability in your projects.

Showcase Your Problem-Solving Skills

TE Connectivity values strong problem-solving capabilities. Prepare examples from your past experiences where you faced significant challenges and how you overcame them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly outline the context and the impact of your actions. This will help you convey your results-oriented mindset and fact-based decision-making approach.

Emphasize Collaboration and Communication

Given the collaborative nature of the role, it’s essential to demonstrate your interpersonal and communication skills. Be prepared to discuss how you have worked with cross-functional teams in the past, particularly in Agile environments. Highlight your experience in Scrum methodology and how you have contributed to team dynamics. TE Connectivity values inclusion and teamwork, so showcasing your ability to work effectively with diverse groups will resonate well with the interviewers.

Align with Company Values

TE Connectivity emphasizes values such as integrity, accountability, and innovation. Research the company’s mission and recent initiatives to understand how they align with your personal values and professional goals. During the interview, express your enthusiasm for contributing to a safer, sustainable, and connected world, and provide examples of how you have embodied these values in your work.

Be Ready for Behavioral Questions

In addition to technical questions, expect behavioral questions that assess your fit within the company culture. Prepare to discuss your experiences in managing multiple projects, adapting to changing priorities, and your willingness to flex your schedule for global communications. Reflect on past experiences that demonstrate your adaptability and commitment to delivering results under tight deadlines.

Ask Insightful Questions

At the end of the interview, you will likely have the opportunity to ask questions. Use this time to inquire about the team’s current projects, the challenges they face, and how the Data Engineer role contributes to the company’s strategic goals. This not only shows your interest in the position but also helps you gauge if TE Connectivity is the right fit for you.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at TE Connectivity. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of data architecture and engineering principles. Be prepared to discuss your experience with data pipelines, ETL processes, and cloud technologies, particularly AWS.

Technical Skills

1. Can you explain your experience with ETL processes and the tools you have used?

Understanding your hands-on experience with ETL is crucial, as it is a core responsibility of the role.

How to Answer

Discuss specific ETL tools you have used, the types of data you have worked with, and any challenges you faced during the ETL process.

Example

“I have extensive experience with Talend and SAP Data Services for ETL processes. In my previous role, I designed and implemented ETL workflows to extract data from various sources, transform it for analysis, and load it into our data warehouse. One challenge I faced was optimizing the data transformation process to reduce runtime, which I achieved by implementing parallel processing.”

2. Describe a complex SQL query you have written and the problem it solved.

SQL proficiency is essential for a Data Engineer, and this question assesses your ability to write efficient queries.

How to Answer

Provide context about the data you were working with, the complexity of the query, and the outcome of your efforts.

Example

“I once wrote a complex SQL query that joined multiple tables to generate a comprehensive report on customer purchasing behavior. The query involved several subqueries and aggregations, which allowed us to identify trends and optimize our marketing strategies. This report ultimately led to a 15% increase in targeted campaign effectiveness.”

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

Data quality is critical in data engineering, and this question evaluates your approach to maintaining it.

How to Answer

Discuss specific techniques or tools you use to validate and monitor data quality throughout the pipeline.

Example

“I implement data validation checks at various stages of the data pipeline, including schema validation and data type checks. Additionally, I use monitoring tools to track data quality metrics and set up alerts for any anomalies. This proactive approach has significantly reduced data discrepancies in our reports.”

4. What experience do you have with cloud technologies, particularly AWS?

Given the emphasis on AWS in the job description, this question assesses your familiarity with cloud-based data solutions.

How to Answer

Highlight your experience with AWS services, particularly those relevant to data engineering, such as S3 and Redshift.

Example

“I have worked extensively with AWS, particularly S3 for data storage and Redshift for data warehousing. I designed a data lake architecture using S3 to store raw data and utilized Redshift for analytics. This setup allowed for scalable data processing and improved query performance.”

5. Can you explain the concept of service-oriented architecture (SOA) and how it applies to data integration?

Understanding SOA is important for integrating various data applications and services.

How to Answer

Define SOA and discuss its relevance to data integration, providing examples from your experience.

Example

“Service-oriented architecture (SOA) is a design pattern that allows different services to communicate over a network. In my previous role, I implemented SOA principles to integrate our data services with external APIs, enabling seamless data exchange and improving our overall data architecture.”

Problem-Solving and Analytical Skills

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

This question assesses your problem-solving skills and ability to think critically under pressure.

How to Answer

Provide a specific example of a data-related challenge, the steps you took to resolve it, and the outcome.

Example

“I faced a challenge when our data warehouse was experiencing performance issues due to large data volumes. I conducted a thorough analysis and identified that certain queries were not optimized. I restructured the queries and implemented indexing strategies, which improved performance by 40%.”

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

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any tools or methodologies you use.

Example

“I prioritize tasks based on project deadlines and business impact. I use project management tools like Jira to track progress and ensure that I am meeting deadlines. Additionally, I communicate regularly with stakeholders to align on priorities and adjust as needed.”

3. Can you give an example of how you have used data to drive business decisions?

This question assesses your ability to leverage data for strategic decision-making.

How to Answer

Share a specific instance where your data analysis influenced a business decision.

Example

“I analyzed sales data to identify underperforming products in our inventory. By presenting my findings to the management team, we decided to adjust our marketing strategy and discontinue certain products, which ultimately led to a 20% increase in overall sales.”

4. What strategies do you use to stay updated with the latest data engineering trends and technologies?

This question evaluates your commitment to continuous learning and professional development.

How to Answer

Discuss the resources you use to stay informed about industry trends, such as online courses, webinars, or professional networks.

Example

“I regularly attend webinars and conferences focused on data engineering and cloud technologies. I also follow industry leaders on platforms like LinkedIn and participate in online forums to exchange knowledge with peers. This helps me stay current with emerging trends and best practices.”

5. How do you handle data security and compliance in your projects?

Data security is a critical aspect of data engineering, and this question assesses your awareness of compliance issues.

How to Answer

Discuss your understanding of data security practices and any relevant regulations you adhere to.

Example

“I prioritize data security by implementing encryption for sensitive data both at rest and in transit. I also ensure compliance with regulations such as GDPR by conducting regular audits and maintaining clear documentation of data handling practices.”