XPENG Data Engineer Interview Guide

Getting ready for a Data Engineer interview at XPENG? The XPENG Data Engineer interview process typically spans 5–6 question topics and evaluates skills in areas like distributed systems design, big data architecture, ETL pipeline development, and scalable cloud deployment. Interview preparation is especially important for this role at XPENG, as candidates are expected to demonstrate depth in building robust data platforms that support advanced AI and autonomous driving technologies, and to collaborate effectively with cross-functional teams working on cutting-edge mobility solutions.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Engineer positions at XPENG.
• Gain insights into XPENG’s Data Engineer interview structure and process.
• Practice real XPENG 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 XPENG Data Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

XPENG is a leading smart technology company specializing in the development of electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics, all powered by advanced AI and autonomous driving technologies. The company is committed to revolutionizing intelligent mobility by integrating cutting-edge research in AI, machine learning, and smart connectivity into its products. As a Data Engineer at XPENG, you will contribute directly to the development of big data applications and machine learning platforms that drive innovation in autonomous driving and intelligent transportation solutions.

As a Data Engineer at XPENG, you will design, develop, and maintain big data applications that support Advanced Driver Assistance Systems (ADAS) and other intelligent mobility initiatives. You will work closely with machine learning engineers, data analysts, and infrastructure teams to build and optimize systems such as ETL pipelines, inference platforms, and data mining tools, enhancing the performance and speed of AI-driven features. Your responsibilities include analyzing requirements, implementing distributed systems, deploying solutions to the cloud, and providing ongoing monitoring and user support. This role is critical to advancing XPENG’s mission of pioneering smart, connected vehicles and autonomous technologies through robust data engineering solutions.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough screening of your application and resume by the XPENG recruiting team, focusing on your experience with distributed systems, big data platforms, ETL pipelines, and backend development (especially with Python or Java). Special attention is given to candidates who have worked on high-performance data systems, cloud deployment, and machine learning engineering platforms, particularly in domains such as autonomous driving, ADAS, or smart mobility. To prepare, ensure your resume clearly highlights relevant technical projects, system design contributions, and collaboration with cross-functional teams.

2.2 Stage 2: Recruiter Screen

A recruiter will reach out for an initial phone or video conversation, typically lasting 20–30 minutes. This step aims to assess your motivation for joining XPENG, your understanding of the company’s mission in intelligent mobility, and your general fit for the data engineering team. Expect to discuss your background, interest in smart vehicle technology, and how your experience with big data and distributed systems aligns with XPENG’s technical challenges. Preparation should include familiarizing yourself with XPENG’s technology stack and being ready to articulate your career motivations and relevant experience.

2.3 Stage 3: Technical/Case/Skills Round

This stage involves one or more rounds focused on technical skills and problem-solving. You may encounter live coding exercises, system design challenges (such as designing scalable ETL pipelines, real-time data streaming systems, or database schemas for mobility applications), and case studies involving data cleaning, aggregation, or pipeline troubleshooting. Interviewers, often senior data engineers or engineering managers, will probe your proficiency in backend languages, distributed computing, cloud technologies (like Docker or Kubernetes), and your ability to collaborate with ML and analytics teams. Preparation should include reviewing core data engineering concepts, hands-on coding, and system design best practices relevant to large-scale, real-time data environments.

2.4 Stage 4: Behavioral Interview

A separate behavioral interview evaluates your communication, teamwork, and problem-solving approach. XPENG places emphasis on cross-functional collaboration, adaptability, and clarity in presenting technical insights to both technical and non-technical stakeholders. You may be asked to describe past projects, challenges faced in data initiatives, or how you’ve ensured data quality and reliability in complex systems. Prepare by reflecting on your experiences working in diverse teams, supporting production systems, and making data-driven decisions in ambiguous situations.

2.5 Stage 5: Final/Onsite Round

The final stage typically consists of a series of in-depth interviews with various team members, including senior engineers, data scientists, and sometimes product or analytics leaders. This onsite (or virtual onsite) round covers advanced system design (such as end-to-end data pipelines, real-time analytics platforms, or scalable machine learning infrastructure), technical deep-dives, and scenario-based problem-solving. You may also be asked to present on a previous project or walk through your approach to a complex data engineering challenge. The focus is on assessing your technical depth, architectural thinking, and ability to drive innovation in smart mobility data systems.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll enter the offer stage, where the XPENG recruiting team will discuss compensation, equity, benefits, and role expectations. Offers are tailored based on your experience, technical skillset, and alignment with XPENG’s mission and values. Be prepared to discuss your salary expectations and any logistical considerations, such as relocation or remote work preferences.

2.7 Average Timeline

The typical XPENG Data Engineer interview process takes between 3 to 5 weeks from application to offer. Fast-track candidates with highly relevant experience or referrals may move through the process in as little as 2 weeks, while standard pacing usually involves a week between each stage, depending on interviewer availability and scheduling logistics. Technical and onsite rounds may be clustered into a single day or spread out over several days for convenience.

Next, let’s dive into the specific types of questions that have been asked in the XPENG Data Engineer interview process.

3.1. Data Pipeline & ETL System Design

Data engineering at XPENG demands strong expertise in architecting robust, scalable pipelines for heterogeneous data sources. Expect questions on designing ETL processes, real-time streaming, and troubleshooting pipeline failures. Focus on reliability, scalability, and how your solutions enable business insights.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe how you would handle schema variability, ensure data integrity, and optimize for throughput. Discuss your strategy for incremental loads, error handling, and monitoring.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Outline your approach from raw data ingestion to model deployment, emphasizing modularity and fault tolerance. Highlight how you would automate data validation and retraining.

3.1.3 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Explain your debugging workflow, including logging, root cause analysis, and rollback strategies. Discuss how you would build proactive monitoring and alerting.

3.1.4 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Focus on your validation steps, error handling, and how you would automate schema evolution. Mention how you would ensure data quality and reporting accuracy.

3.1.5 Aggregating and collecting unstructured data.
Discuss techniques for handling unstructured sources, such as logs or media files, and extracting actionable information. Emphasize your use of distributed processing frameworks and metadata management.

3.2. Database Design & Data Modeling

Strong data modeling skills are essential for building efficient, maintainable databases at XPENG. You’ll be asked about schema design, normalization, and handling large-scale transactional or analytical workloads. Demonstrate your ability to balance performance, flexibility, and data consistency.

3.2.1 Design a database for a ride-sharing app.
Describe key entities, relationships, and indexing strategies. Explain how you would handle high write volume and ensure data consistency across services.

3.2.2 Design a data warehouse for a new online retailer.
Outline your approach to dimensional modeling, partitioning, and ETL integration. Discuss how you would support flexible reporting and growth.

3.2.3 How would you determine which database tables an application uses for a specific record without access to its source code?
Explain your investigative process using metadata, query logs, and schema analysis. Highlight your ability to reverse-engineer dependencies.

3.2.4 Design the system supporting an application for a parking system.
Discuss your approach to schema design, concurrency, and ensuring real-time availability. Mention how you would scale as the user base grows.

3.2.5 Design a solution to store and query raw data from Kafka on a daily basis.
Describe your storage architecture, indexing, and query optimization strategies. Highlight how you would handle schema evolution and data retention.

3.3. Data Quality & Cleaning

XPENG expects data engineers to be proactive in maintaining data quality and reliability. You’ll face questions about handling messy, incomplete, or inconsistent datasets, and designing processes for ongoing quality assurance. Focus on reproducibility, transparency, and communication with stakeholders.

3.3.1 Describing a real-world data cleaning and organization project.
Walk through your cleaning workflow, including profiling, deduplication, and validation steps. Emphasize documentation and reproducibility.

3.3.2 How would you approach improving the quality of airline data?
Discuss strategies for identifying and fixing data issues, implementing monitoring, and collaborating with upstream teams. Highlight metrics to measure improvement.

3.3.3 Ensuring data quality within a complex ETL setup.
Explain your approach to testing, anomaly detection, and automating quality checks. Mention how you would communicate issues and resolutions.

3.3.4 Write a query to get the current salary for each employee after an ETL error.
Describe how you would identify and correct data inconsistencies, ensuring accuracy in reporting. Discuss rollback and audit strategies.

3.3.5 Describing a data project and its challenges.
Share how you overcame obstacles such as incomplete data, shifting requirements, or technical bottlenecks. Focus on adaptability and stakeholder management.

3.4. Programming & Algorithmic Thinking

Data engineers at XPENG must demonstrate strong coding skills, particularly for data manipulation, sampling, and automation. Expect questions on writing functions, optimizing queries, and choosing the right tools for the task. Show clarity in your logic and efficiency in your solutions.

3.4.1 Write a function to return the names and ids for ids that we haven't scraped yet.
Explain your approach to set operations and efficient lookups, considering large datasets.

3.4.2 Write a function to get a sample from a Bernoulli trial.
Describe your logic for simulating binary outcomes and parameterizing probability.

3.4.3 Write a function to get a sample from a standard normal distribution.
Discuss how you would use libraries or implement sampling algorithms, ensuring reproducibility.

3.4.4 Write a function to find how many friends each person has.
Focus on your use of aggregation and efficient data structures to scale to large social graphs.

3.4.5 python-vs-sql
Compare scenarios where Python or SQL is more appropriate, considering performance, readability, and maintainability.

3.5. Data Analytics & Business Impact

You’ll be asked to bridge technical work with business outcomes at XPENG. Prepare to discuss how you extract insights, communicate findings, and measure the impact of your data engineering solutions. Highlight your ability to tailor communication to different audiences.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your strategy for simplifying technical findings and adapting to stakeholder needs.

3.5.2 Making data-driven insights actionable for those without technical expertise
Share how you translate analytics into practical recommendations, using clear language and visuals.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Discuss your approach to designing intuitive dashboards and training materials.

3.5.4 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Explain your experimental design, KPI selection, and communication of results to leadership.

3.5.5 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time
Describe your approach to real-time data aggregation, visualization, and alerting.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on how your analysis led to a concrete business outcome. Detail the problem, your data-driven approach, and the impact.

3.6.2 Describe a challenging data project and how you handled it.
Highlight the obstacles, your problem-solving tactics, and how you collaborated across teams to deliver results.

3.6.3 How do you handle unclear requirements or ambiguity?
Emphasize your communication strategy, iterative development, and how you validate assumptions with stakeholders.

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Show your ability to listen, facilitate discussion, and find common ground while advocating for data-driven solutions.

3.6.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe how you tailored your message, used visualizations, or sought feedback to bridge the gap.

3.6.6 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?
Explain your prioritization framework, transparent communication, and how you balanced stakeholder needs with project constraints.

3.6.7 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Discuss how you broke down deliverables, communicated risks, and ensured transparency.

3.6.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Share how you built trust, presented compelling evidence, and navigated organizational dynamics.

3.6.9 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Outline your prioritization criteria, stakeholder alignment process, and how you maintained focus on strategic goals.

3.6.10 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Describe your approach to missing data, how you communicated uncertainty, and the business impact of your analysis.

4.1 Company-specific tips:

Learn XPENG’s mission and recent innovations in intelligent mobility, including their advancements in electric vehicles, eVTOL aircraft, and robotics powered by AI and autonomous driving. Be ready to articulate how data engineering directly supports these initiatives, especially in terms of enabling machine learning and real-time analytics for smart vehicles.

Familiarize yourself with the challenges of big data in the automotive and mobility sector. XPENG’s data engineers work with massive, heterogeneous data sources from vehicles, sensors, and user interactions. Prepare to discuss how you would handle data at scale, ensure low-latency processing, and support high-reliability systems for safety-critical applications.

Demonstrate awareness of XPENG’s tech stack. While specific technologies may evolve, expect to discuss distributed systems, cloud platforms (such as Kubernetes or Docker), and the integration of data infrastructure with AI/ML pipelines. Be prepared to explain how your experience aligns with XPENG’s focus on scalable, production-grade solutions for autonomous driving and connected vehicles.

Showcase your ability to work cross-functionally. XPENG values collaboration between data engineers, ML engineers, product managers, and analytics teams. Prepare examples of how you’ve partnered with diverse teams to deliver robust data solutions, and be ready to discuss how you communicate technical concepts to non-technical stakeholders.

4.2 Role-specific tips:

Master ETL pipeline design and troubleshooting.
XPENG’s interview process often includes system design questions focused on building scalable ETL pipelines for heterogeneous and unstructured data sources, such as vehicle telemetry, logs, and user data. Practice explaining your approach to schema evolution, data validation, error handling, and monitoring. Be ready to discuss how you would automate incremental loads, recover from failures, and maintain data integrity in a high-throughput environment.

Demonstrate expertise in distributed systems and big data frameworks.
You’ll be expected to design robust data architectures using distributed processing tools like Spark, Flink, or Hadoop. Prepare to discuss how you would optimize for performance, handle data partitioning, and ensure fault tolerance. Highlight your experience with cloud deployment and how you’ve scaled data systems to support real-time analytics or machine learning workloads.

Show strong data modeling and database design skills.
XPENG looks for data engineers who can create efficient, maintainable schemas for both transactional and analytical workloads. Prepare to walk through your process for designing normalized and denormalized models, indexing strategies, and supporting high write/read volumes. Be ready to discuss how you would handle schema changes, data consistency, and performance optimization in a rapidly growing environment.

Highlight your data quality and cleaning strategies.
Maintaining high data quality is critical at XPENG, especially when supporting AI-driven features in autonomous vehicles. Be prepared to describe your approach to profiling, deduplication, validation, and anomaly detection. Share examples of how you’ve implemented automated quality checks and communicated issues to upstream teams, emphasizing reproducibility and transparency.

Exhibit advanced programming and algorithmic problem-solving.
Expect to write code in Python or Java to manipulate large datasets, implement sampling algorithms, or automate data tasks. Practice explaining your logic clearly, optimizing for efficiency, and choosing the right language or tool for the job. Be ready to compare scenarios where SQL or Python is more appropriate and justify your decisions.

Connect technical solutions to business impact.
XPENG values engineers who understand how their work drives business outcomes. Prepare to discuss how you’ve translated complex data into actionable insights, designed dashboards for real-time monitoring, and measured the impact of your solutions. Practice tailoring your communication style for both technical and executive audiences, using clear language and visualizations.

Prepare for behavioral questions focused on collaboration and adaptability.
XPENG’s culture emphasizes teamwork, adaptability, and data-driven decision-making. Reflect on past experiences where you navigated ambiguity, negotiated priorities, or influenced stakeholders without formal authority. Be ready to share stories that showcase your problem-solving skills, resilience, and commitment to delivering value in fast-paced, cross-functional environments.

5.1 How hard is the XPENG Data Engineer interview?
The XPENG Data Engineer interview is challenging and designed to rigorously assess your expertise in distributed systems, big data architecture, scalable ETL pipeline development, and cloud deployment. Expect in-depth technical rounds, system design challenges, and behavioral questions focused on collaboration and adaptability. Candidates with hands-on experience in building robust data platforms for AI, autonomous driving, or smart mobility will find the interview demanding but highly rewarding.

5.2 How many interview rounds does XPENG have for Data Engineer?
XPENG typically conducts 5 to 6 interview rounds for Data Engineer candidates. The stages include an initial resume review, recruiter screen, technical/case/skills rounds, behavioral interview, a final onsite (or virtual onsite) round with multiple team members, and an offer/negotiation stage. Each round is tailored to evaluate both technical depth and cultural fit.

5.3 Does XPENG ask for take-home assignments for Data Engineer?
While XPENG’s process is primarily focused on live technical interviews and system design exercises, some candidates may be given take-home assignments or case studies, especially for pipeline troubleshooting or data modeling scenarios. These assignments are designed to test your practical skills in solving real-world data engineering problems relevant to XPENG’s business.

5.4 What skills are required for the XPENG Data Engineer?
Key skills for XPENG Data Engineers include advanced proficiency in distributed systems, big data frameworks (such as Spark, Hadoop, or Flink), ETL pipeline design and troubleshooting, database modeling, cloud deployment (Kubernetes, Docker), programming (Python or Java), and data quality assurance. Experience in supporting machine learning platforms and collaborating with cross-functional teams in mobility or autonomous driving domains is highly valued.

5.5 How long does the XPENG Data Engineer hiring process take?
The XPENG Data Engineer hiring process typically takes 3 to 5 weeks from application to offer. Fast-track candidates may complete the process in as little as 2 weeks, while the standard timeline usually involves about a week between each stage, depending on interviewer availability and scheduling logistics.

5.6 What types of questions are asked in the XPENG Data Engineer interview?
Expect a mix of technical and behavioral questions, including system design for scalable ETL pipelines, real-time data streaming, database modeling, data cleaning and quality assurance, coding exercises (Python/Java/SQL), troubleshooting pipeline failures, and scenario-based problem-solving. Behavioral rounds focus on teamwork, communication, and adaptability in cross-functional environments.

5.7 Does XPENG give feedback after the Data Engineer interview?
XPENG generally provides high-level feedback through recruiters after each interview round. While detailed technical feedback may be limited, you can expect insights on your overall performance and fit for the team. XPENG values transparency and encourages candidates to seek clarification if needed.

5.8 What is the acceptance rate for XPENG Data Engineer applicants?
The Data Engineer role at XPENG is highly competitive, with an estimated acceptance rate of 3–5% for qualified applicants. Candidates with strong backgrounds in big data, distributed systems, and experience in the mobility or autonomous driving sector have a distinct advantage.

5.9 Does XPENG hire remote Data Engineer positions?
XPENG does offer remote opportunities for Data Engineers, particularly for roles focused on cloud deployment and distributed data systems. Some positions may require occasional in-person collaboration or travel, especially for projects involving hardware integration or cross-team initiatives. Always clarify remote work expectations during the interview process.

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

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