National Instruments Data Engineer Interview Guide

Getting ready for a Data Engineer interview at National Instruments? The National Instruments Data Engineer interview process typically spans several question topics and evaluates skills in areas like data pipeline design, ETL systems, data cleaning and organization, technical presentations, and communicating complex insights to diverse audiences. Interview preparation is especially important for this role at National Instruments, as candidates are expected to demonstrate not only technical proficiency but also the ability to articulate their engineering thought process and collaborate across multidisciplinary teams in a hardware-software integrated environment.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Engineer positions at National Instruments.
• Gain insights into National Instruments’ Data Engineer interview structure and process.
• Practice real National Instruments Data Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the National Instruments Data Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

National Instruments (NI) is a global leader in automated test and measurement systems, providing software-connected solutions that accelerate innovation and productivity for engineers and scientists across industries such as automotive, aerospace, electronics, and manufacturing. NI’s platforms enable organizations to develop, test, and deploy complex systems efficiently and reliably. With a strong focus on data-driven insights and modular hardware-software integration, NI empowers customers to solve engineering challenges and advance technological progress. As a Data Engineer, you will contribute to optimizing data infrastructure and analytics, supporting NI’s mission to drive innovation through high-quality, actionable data.

As a Data Engineer at National Instruments, you are responsible for designing, building, and maintaining scalable data pipelines that support the company’s engineering and business operations. You will work closely with software developers, data analysts, and product teams to ensure reliable data collection, storage, and processing from a variety of sources. Core tasks include optimizing data architecture, implementing ETL processes, and ensuring data quality for analytics and reporting. This role is essential for enabling data-driven decision-making across the organization, contributing to the development of innovative test and measurement solutions that National Instruments provides to its customers.

2.1 Stage 1: Application & Resume Review

The initial step for Data Engineer candidates at National Instruments typically involves submitting an online application or connecting with a recruiter at a campus or industry career fair. During this stage, resumes are screened for relevant experience in data engineering, including expertise in data pipelines, ETL processes, Python programming, database design, and familiarity with algorithms and data structures. Candidates with strong academic backgrounds or practical project experience in data management, analytics, or engineering are prioritized. To prepare, ensure your resume clearly highlights hands-on experience with data systems, technical skills, and any project or internship work that demonstrates your ability to solve real-world data challenges.

2.2 Stage 2: Recruiter Screen

Selected candidates are contacted by a recruiter for a brief phone screen, which typically lasts 15–30 minutes. This conversation centers on your background, motivation for applying, and understanding of the Data Engineer role at National Instruments. The recruiter may also touch on your experience with data cleaning, pipeline development, and your ability to communicate technical concepts to non-technical audiences. Preparation should focus on articulating your interest in National Instruments, your relevant skills, and your ability to work in multidisciplinary teams.

2.3 Stage 3: Technical/Case/Skills Round

The technical assessment phase often consists of a phone or on-campus interview, lasting 45–60 minutes, and may include a mix of whiteboard coding, algorithmic problem solving, and system design questions. You may be asked to hand-write code (often in Python), discuss your approach to building scalable data pipelines, design ETL solutions, or solve real-world data engineering scenarios such as database schema design, data ingestion, and troubleshooting pipeline failures. Emphasis is placed on your problem-solving process, clarity of thought, and ability to explain decisions. Prepare by practicing data structure algorithms, reviewing data modeling concepts, and being ready to walk through your reasoning step-by-step.

2.4 Stage 4: Behavioral Interview

Behavioral interviews are designed to assess cultural fit, teamwork, and communication skills. Questions often follow the “tell me about a time when…” format and may probe your experience collaborating across functions, resolving project challenges, or presenting complex data insights to diverse audiences. Interviewers look for confidence, adaptability, and the ability to make data accessible to non-technical stakeholders. Prepare by reflecting on past projects where you navigated ambiguity, overcame technical hurdles, or led data-driven initiatives, and practice structuring your responses using frameworks like STAR (Situation, Task, Action, Result).

2.5 Stage 5: Final/Onsite Round

The onsite round is typically a full-day event at National Instruments’ headquarters and includes a series of interviews with multiple teams—such as R&D, Systems, and Technical groups—as well as HR. Candidates can expect a mix of technical and behavioral panels, with each session lasting 30–60 minutes. A unique aspect of this round is a required technical presentation: you’ll be asked to prepare and deliver a 10-minute presentation on a data or engineering topic of your choice, demonstrating your ability to communicate complex ideas clearly and adapt your message to the audience. There may also be informal interactions, such as facility tours or group discussions, to further assess fit and collaboration skills. To prepare, select a technical topic you know well, practice delivering your presentation to both technical and non-technical listeners, and be ready for deep dives into your project experience.

2.6 Stage 6: Offer & Negotiation

After the final onsite round, successful candidates are contacted by the recruiter to discuss the offer package, including compensation, benefits, and start date. This stage may involve additional discussions with HR to clarify role expectations or address any remaining questions. Be prepared to negotiate thoughtfully and express your continued enthusiasm for the opportunity.

2.7 Average Timeline

The typical interview process for a Data Engineer at National Instruments spans 3–6 weeks from initial application to offer, though timelines can vary. Fast-track candidates—such as those who connect at career fairs or have highly relevant experience—may progress through the process in as little as 2–3 weeks, while standard timelines involve about a week between each stage. The onsite round is usually scheduled several weeks in advance, and offer decisions are communicated within one to two weeks after the final interviews. Delays can occur, especially around scheduling or during high-volume recruiting seasons, so proactive communication with recruiters is helpful.

Next, let’s dive into the specific interview questions you may encounter during the National Instruments Data Engineer interview process.

3.1 Data Engineering & ETL Design

Data engineering interviews at National Instruments often focus on your ability to architect robust, scalable pipelines and ensure data quality across diverse sources. Expect questions that test your understanding of ETL processes, warehouse design, and how you handle large-scale data movement and transformation.

3.1.1 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Describe your approach to pipeline architecture, including validation, error handling, and scalability. Emphasize modular design and automation for repeatability.

3.1.2 Let's say that you're in charge of getting payment data into your internal data warehouse.
Discuss the ingestion, transformation, and loading steps, highlighting how you ensure data integrity and real-time or batch processing needs.

3.1.3 Design a data warehouse for a new online retailer
Outline your schema design, partitioning strategy, and how you support analytics and reporting. Address scalability, normalization, and indexing.

3.1.4 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Explain your method for handling varying data formats, error recovery, and maintaining pipeline reliability at scale.

3.1.5 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Describe your tool selection process, focusing on cost-effectiveness, integration, and long-term maintainability.

3.2 Data Quality, Cleaning & Transformation

Ensuring high data quality is a core competency for data engineers at National Instruments. Be ready to discuss your strategies for detecting, diagnosing, and resolving data issues, as well as how you automate routine cleaning tasks.

3.2.1 Describing a real-world data cleaning and organization project
Detail your process for identifying and resolving inconsistencies, missing values, and duplicates, and the impact on downstream analytics.

3.2.2 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Share your troubleshooting framework, including monitoring, logging, and root cause analysis, as well as preventive measures.

3.2.3 Ensuring data quality within a complex ETL setup
Describe your approach to validation, reconciliation, and documentation to maintain trust in your pipelines.

3.2.4 How would you approach improving the quality of airline data?
Discuss profiling techniques, data governance, and implementing automated checks to catch errors early.

3.2.5 Modifying a billion rows
Explain your strategy for efficiently updating massive datasets, considering performance, downtime, and rollback plans.

3.3 Data Modeling & System Design

This topic assesses your ability to design data models and systems that support complex business requirements. Expect to demonstrate your knowledge in schema design, normalization, and building scalable solutions.

3.3.1 System design for a digital classroom service.
Outline the entities, relationships, and data flow required, focusing on scalability and flexibility for evolving requirements.

3.3.2 Design a database for a ride-sharing app.
Describe your schema choices, indexing, and considerations for real-time data needs and high transaction volumes.

3.3.3 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Walk through ingestion, processing, storage, and serving layers, highlighting how you support machine learning workflows.

3.3.4 Design a data pipeline for hourly user analytics.
Explain your aggregation strategy, data retention policies, and how you enable low-latency queries.

3.4 Data Analysis & SQL

Data engineers at National Instruments are expected to be proficient in SQL and capable of deriving insights from large datasets. Prepare to write queries and discuss your approach to data analysis.

3.4.1 Write a SQL query to count transactions filtered by several criterias.
Break down your filtering logic, use of aggregate functions, and optimization for large tables.

3.4.2 Select the 2nd highest salary in the engineering department
Demonstrate your understanding of ranking/window functions and efficient subqueries.

3.4.3 Reporting of Salaries for each Job Title
Describe your grouping and aggregation approach, and how you’d ensure accuracy with incomplete data.

3.4.4 Find the total salary of slacking employees.
Explain your filtering criteria and aggregation method, and address potential edge cases.

3.5 Communication & Data Accessibility

Effective communication and making data accessible to non-technical users is highly valued. Expect questions on how you tailor your messaging and visualization for different audiences.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss storytelling techniques, visualization choices, and how you adapt your delivery based on stakeholder needs.

3.5.2 Demystifying data for non-technical users through visualization and clear communication
Share examples of simplifying technical findings and using dashboards or reports to drive business decisions.

3.5.3 Making data-driven insights actionable for those without technical expertise
Explain how you bridge the gap between complex analytics and practical recommendations.

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3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe the business problem, how you analyzed the data, and the impact your recommendation had.

3.6.2 Describe a challenging data project and how you handled it.
Focus on obstacles, your problem-solving approach, and the final outcome.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your process for clarifying objectives and iterating with stakeholders.

3.6.4 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss your approach to missing data, methods for imputation or exclusion, and how you communicated uncertainty.

3.6.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Highlight your prioritization framework and communication strategy to align stakeholders.

3.6.6 Walk us through how you built a quick-and-dirty de-duplication script on an emergency timeline.
Share your approach to rapid problem-solving, balancing speed with data integrity.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe how you built consensus, communicated value, and drove adoption.

3.6.8 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Explain the tools or processes you put in place and the impact on workflow reliability.

3.6.9 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Discuss your time management techniques, tools you use, and how you communicate priorities with your team.

3.6.10 Tell us about a time you proactively identified a business opportunity through data.
Describe how you discovered the opportunity, validated it with data, and influenced decision-makers.

4.1 Company-specific tips:

Familiarize yourself with National Instruments’ core business areas, especially how automated test and measurement systems generate, collect, and utilize large-scale data. Understand the importance of data integrity and reliability in hardware-software integrated environments, where data pipelines must support both engineering and business operations. Review recent product launches, customer case studies, and NI’s approach to modular platforms, as this context will help you tailor your technical solutions and communication to their unique challenges.

Demonstrate your ability to collaborate across multidisciplinary teams. At National Instruments, Data Engineers routinely work with hardware engineers, software developers, and business stakeholders. Prepare examples of cross-functional projects where you translated technical requirements into actionable data solutions, and be ready to discuss how you adapt your communication style for technical and non-technical audiences.

Stay current on industry trends in test automation, data analytics, and cloud integration. National Instruments values candidates who can help drive innovation, so be prepared to discuss how emerging technologies—such as real-time data streaming, cloud-based ETL, or machine learning—can be leveraged to improve data infrastructure and analytics within their ecosystem.

4.2 Role-specific tips:

4.2.1 Practice designing scalable data pipelines for hardware-generated data.
Focus on building robust ETL systems that can ingest, parse, and process data from diverse sources, including sensors, instruments, and legacy systems. Be ready to discuss your approach to validation, error handling, and maintaining data quality as data moves from raw ingestion to storage and reporting.

4.2.2 Prepare to optimize data storage and retrieval for analytics and reporting.
Review data warehouse and database design principles, including schema normalization, partitioning, and indexing. Be able to explain how you would design a data warehouse for high-volume, high-variety engineering data, and how you ensure fast, reliable access for analytics and reporting.

4.2.3 Demonstrate expertise in data cleaning and transformation.
National Instruments places a premium on data quality, so practice identifying and resolving inconsistencies, duplicates, and missing values in large datasets. Be ready to discuss automated cleaning processes and how you ensure that downstream analytics are based on trustworthy data.

4.2.4 Show your troubleshooting skills for pipeline reliability.
Prepare to describe your framework for diagnosing and resolving failures in batch or real-time data pipelines. Emphasize your use of monitoring, logging, and root cause analysis, as well as strategies for preventing recurrence and minimizing downtime.

4.2.5 Exhibit strong SQL and analytical skills.
Be ready to write and optimize SQL queries that aggregate, filter, and analyze engineering and business data. Practice using window functions, subqueries, and advanced joins to solve reporting and analysis challenges, and be able to explain your logic clearly.

4.2.6 Communicate complex technical insights in accessible ways.
National Instruments expects Data Engineers to present findings and recommendations to both technical and non-technical audiences. Prepare examples of how you’ve used storytelling, visualization, and clear language to make data actionable for stakeholders with varying levels of expertise.

4.2.7 Prepare a technical presentation on a data engineering topic.
For the onsite round, select a topic that showcases your depth in data engineering—such as building a scalable ETL pipeline, optimizing data quality, or integrating hardware data into cloud analytics. Practice delivering your presentation to both technical and non-technical listeners, and anticipate follow-up questions that probe your reasoning and adaptability.

4.2.8 Reflect on behavioral competencies like teamwork, adaptability, and stakeholder management.
Think through examples where you navigated ambiguity, negotiated scope changes, or influenced stakeholders without formal authority. Use structured frameworks like STAR to organize your stories and highlight your impact.

4.2.9 Prepare to discuss time management and organization strategies.
National Instruments projects often involve multiple deadlines and shifting priorities. Be ready to share your approach to balancing competing tasks, staying organized, and communicating your progress and priorities with your team.

4.2.10 Proactively identify business opportunities through data.
Show that you can go beyond technical execution by spotting patterns, proposing new solutions, and driving innovation through data-driven insights. Prepare examples where your analysis led to measurable improvements or opened up new opportunities for your team or company.

5.1 How hard is the National Instruments Data Engineer interview?
The National Instruments Data Engineer interview is considered moderately challenging, especially for those new to hardware-software integrated environments. The process tests both your technical depth in data engineering—such as designing scalable pipelines, optimizing ETL systems, and ensuring data quality—and your ability to communicate complex insights to multidisciplinary teams. Candidates with hands-on experience in data infrastructure, troubleshooting, and collaboration across engineering and analytics functions have a distinct advantage.

5.2 How many interview rounds does National Instruments have for Data Engineer?
Typically, the National Instruments Data Engineer interview process includes five to six rounds: an initial application and resume review, a recruiter screen, a technical/case round, a behavioral interview, a final onsite round (which features a technical presentation), and an offer/negotiation stage.

5.3 Does National Instruments ask for take-home assignments for Data Engineer?
Take-home assignments are not a standard part of the National Instruments Data Engineer process, but candidates may be asked to prepare a technical presentation for the onsite round. This presentation should showcase your expertise in a data engineering topic and demonstrate your ability to communicate complex ideas to both technical and non-technical audiences.

5.4 What skills are required for the National Instruments Data Engineer?
Key skills include designing and building scalable data pipelines, advanced ETL processes, data cleaning and transformation, strong SQL proficiency, data modeling, troubleshooting pipeline reliability, and effective communication of technical concepts. Familiarity with hardware-generated data, cloud integration, and cross-functional collaboration is highly valued.

5.5 How long does the National Instruments Data Engineer hiring process take?
The typical timeline for the National Instruments Data Engineer interview process is 3–6 weeks from application to offer. Fast-track candidates may complete the process in as little as 2–3 weeks, while standard timelines involve about a week between each stage. Scheduling and high-volume recruiting periods may cause some variation.

5.6 What types of questions are asked in the National Instruments Data Engineer interview?
Expect a mix of technical and behavioral questions. Technical topics include designing ETL pipelines, troubleshooting data quality issues, database schema design, SQL coding, and system design for engineering data. Behavioral questions assess teamwork, adaptability, stakeholder management, and your ability to make data accessible to non-technical users. The onsite round features a technical presentation on a data engineering topic of your choice.

5.7 Does National Instruments give feedback after the Data Engineer interview?
National Instruments typically provides high-level feedback via recruiters, especially after onsite interviews. While detailed technical feedback may be limited, you can expect to hear whether your skills and experience align with the role’s requirements and company culture.

5.8 What is the acceptance rate for National Instruments Data Engineer applicants?
While exact acceptance rates are not publicly available, the Data Engineer role at National Instruments is competitive. Given the emphasis on both technical and communication skills, it’s estimated that 5–10% of qualified applicants move forward to the offer stage.

5.9 Does National Instruments hire remote Data Engineer positions?
National Instruments does offer remote Data Engineer positions, though some roles may require periodic visits to headquarters or collaboration with onsite teams, especially when working with hardware-software integrated systems. Be sure to clarify remote work expectations with your recruiter during the process.

• Related guides:
  • National Instruments interview questions
  • Data Engineer interview guide
  • Top data engineering interview tips

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