Crexi Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Crexi? The Crexi Data Engineer interview process typically spans multiple question topics and evaluates skills in areas like data pipeline design, SQL and Python proficiency, cloud data architecture, and real-world problem solving. Interview preparation is especially important for this role at Crexi, as candidates are expected to demonstrate expertise in building scalable data systems, optimizing big data workflows, and translating complex data challenges into actionable solutions within the commercial real estate technology space.

In preparing for the interview, you should:

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

Crexi is a leading technology company transforming the commercial real estate industry with innovative software solutions that empower professionals to market, analyze, and transact properties more efficiently. Serving millions of monthly users, Crexi provides a robust platform with due diligence tools, transaction services, deal pipeline support, and a dynamic listing marketplace, facilitating over $615 billion in closed deals and marketing more than $7 trillion in property value. As a Data Engineer, you will help build and optimize scalable data pipelines and architectures, directly supporting Crexi’s mission to accelerate deal efficiency and enhance the commercial real estate transaction process.

As a Data Engineer at Crexi, you will design, build, and optimize scalable data pipelines and architectures that power the company’s commercial real estate platform. You’ll work with a range of databases and technologies—including SQL, NoSQL, AWS, Snowflake, and tools like Airflow and DBT—to transform and manage large datasets. Your responsibilities include developing robust data workflows, ensuring data quality and reliability, troubleshooting data issues, and collaborating with cross-functional teams to deliver actionable insights. This role is crucial for supporting Crexi’s industry-leading software, enabling efficient property marketing, analysis, and transactions for millions of users in the commercial real estate sector.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough review of your application and resume by Crexi’s talent acquisition team, focusing on your experience building and optimizing data pipelines, proficiency with SQL and Python, familiarity with cloud-based data architectures (especially AWS and Snowflake), and your track record with scalable data engineering solutions. Highlighting hands-on experience with tools like Airflow, Kafka, Spark, and your ability to manage ETL pipelines and data quality will increase your chances of progressing. Preparing a tailored resume that demonstrates your impact in previous data engineering roles is crucial at this stage.

2.2 Stage 2: Recruiter Screen

This stage is typically a 30-minute phone conversation with a recruiter. Expect to discuss your background, motivation for joining Crexi, and your alignment with the company’s mission of transforming commercial real estate through technology. The recruiter will also assess your communication skills and clarify your experience with data engineering best practices, cloud services, and your familiarity with both SQL and NoSQL databases. Prepare by reviewing your career narrative and being ready to explain why your skills are well-suited for Crexi’s data-driven environment.

2.3 Stage 3: Technical/Case/Skills Round

Conducted by a data engineering manager or senior team member, this round evaluates your technical expertise through a mix of live coding, system design, and scenario-based questions. You may be asked to design robust, scalable data pipelines (e.g., for ingesting or transforming large datasets), demonstrate advanced SQL querying, or discuss your approach to data quality, error handling, and monitoring. Familiarity with cloud data warehouses (like Snowflake), orchestration tools (Airflow, DBT), stream processing (Kafka, Spark), and handling real-world data challenges is essential. Reviewing core data structures, algorithms, and presenting past projects where you solved complex data engineering problems will help you excel.

2.4 Stage 4: Behavioral Interview

In this stage, you’ll meet with cross-functional stakeholders or data team leads who will assess your collaboration, communication, and problem-solving abilities. Expect to discuss how you’ve managed data pipeline failures, addressed data quality issues, and worked with non-technical partners to deliver actionable insights. You may also be asked about your strengths, weaknesses, and approach to presenting technical concepts to diverse audiences. Prepare examples that showcase adaptability, clear communication, and your ability to drive improvements in data engineering processes.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of several back-to-back interviews (virtual or onsite) with data engineers, product managers, and possibly executives. This round dives deeper into your technical acumen (such as system design for real-time streaming and data warehousing), business sense (aligning data solutions with company goals), and culture fit. You may be asked to whiteboard end-to-end pipeline architectures, address hypothetical data incidents, or discuss trade-offs between different data technologies. Demonstrating a holistic understanding of Crexi’s business and your ability to build scalable, reliable data systems will set you apart.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive an offer from Crexi’s recruiting team. This stage includes discussions around compensation, benefits, start date, and any remaining questions about the role or company. Being prepared to articulate your value, clarify expectations, and negotiate based on your experience and market standards will help ensure a smooth transition to the team.

2.7 Average Timeline

The typical Crexi Data Engineer interview process spans 3–4 weeks from application to offer, with each round scheduled about a week apart. Candidates with highly relevant experience or strong referrals may move through the process faster, while standard pacing allows for more in-depth evaluation and scheduling flexibility. The technical and onsite rounds are often prioritized for quick turnaround, especially when the team is eager to fill a critical role.

Next, let’s dive into the specific types of interview questions you can expect during the Crexi Data Engineer interview process.

3.1. Data Pipeline Design & ETL

As a Data Engineer at Crexi, you’ll be expected to architect, optimize, and troubleshoot scalable data pipelines for both batch and real-time analytics. Focus on demonstrating your ability to build robust ETL systems, handle diverse data sources, and ensure data quality and reliability under production constraints.

3.1.1 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Explain your approach to ingestion, parsing, error handling, and reporting. Highlight choices around distributed processing, schema validation, and how you ensure reliability at scale.

3.1.2 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Discuss how you’d manage diverse schemas, automate data normalization, and guarantee end-to-end data integrity. Emphasize your strategies for monitoring, error recovery, and extensibility.

3.1.3 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Describe your troubleshooting workflow, including log analysis, root cause identification, and remediation planning. Mention tools for alerting and how you’d prevent recurrence.

3.1.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes
Lay out the steps from data ingestion to model deployment, focusing on data validation, feature engineering, and serving predictions efficiently.

3.1.5 Redesign batch ingestion to real-time streaming for financial transactions
Explain your migration strategy, technology choices (e.g., Kafka, Spark Streaming), and how you’d maintain data consistency and low latency.

3.2. Data Warehouse & System Architecture

You’ll be tasked with designing scalable, maintainable data warehouses and architecting systems to support analytics and reporting. Show your expertise in schema design, partitioning strategies, and supporting international growth.

3.2.1 Design a data warehouse for a new online retailer
Present a high-level schema, discuss fact/dimension tables, and how you’d support rapid expansion and analytics.

3.2.2 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Focus on handling localization, currency conversion, and regulatory compliance in your design.

3.2.3 System design for a digital classroom service
Outline the architecture for scalability, privacy, and multi-tenant support. Discuss trade-offs between relational and NoSQL solutions.

3.2.4 Design a database for a ride-sharing app
Describe schema choices for users, rides, drivers, and real-time updates. Address data consistency and query efficiency.

3.3. Data Quality, Cleaning & Reliability

Maintaining high data quality is critical at Crexi. Prepare to discuss your strategies for cleaning, profiling, and validating large, messy datasets, as well as your approach to automating these processes.

3.3.1 Describing a real-world data cleaning and organization project
Walk through your process for profiling, cleaning, and validating data. Highlight tools, techniques, and how you ensured reproducibility.

3.3.2 How would you approach improving the quality of airline data?
Explain your framework for identifying root causes, prioritizing fixes, and measuring improvements.

3.3.3 Ensuring data quality within a complex ETL setup
Discuss monitoring strategies, automated validation, and handling edge cases in multi-source pipelines.

3.3.4 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Outline your approach to reconciling discrepancies, including data lineage analysis and stakeholder collaboration.

3.4. SQL & Data Manipulation

Strong SQL skills are essential for extracting and transforming data efficiently. Expect to demonstrate your ability to write performant queries, handle large datasets, and optimize for speed and accuracy.

3.4.1 Write a SQL query to count transactions filtered by several criterias
Describe your filtering logic, use of indexes, and how you’d optimize for performance on production-scale data.

3.4.2 Write a function to return the names and ids for ids that we haven't scraped yet
Explain your approach for identifying missing records and efficiently processing large tables.

3.4.3 Modifying a billion rows
Discuss best practices for bulk updates, minimizing downtime, and ensuring data integrity.

3.5. Communication & Stakeholder Collaboration

Data Engineers at Crexi must make complex insights accessible to non-technical stakeholders and collaborate across teams. Be ready to show how you tailor your communication and translate analytics into actionable business outcomes.

3.5.1 Making data-driven insights actionable for those without technical expertise
Describe your strategy for translating technical results into clear recommendations, using visualizations and analogies.

3.5.2 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss techniques for adjusting depth and detail based on audience, and how you ensure engagement.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Share examples of dashboards or reports you’ve built that improved decision-making for business users.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Share a specific example where your analysis directly influenced a business outcome, detailing your thought process and impact.

3.6.2 Describe a challenging data project and how you handled it.
Highlight a project with technical hurdles, your approach to problem-solving, and the lessons learned.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your process for clarifying goals, collaborating with stakeholders, and iterating on solutions.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe the communication barriers and the steps you took to build understanding and trust.

3.6.5 You’re given a dataset that’s full of duplicates, null values, and inconsistent formatting. The deadline is soon, but leadership wants insights from this data for tomorrow’s decision-making meeting. What do you do?
Discuss your triage strategy, prioritizing critical cleaning steps, and how you communicate data caveats.

3.6.6 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Share your approach to handling missing data, the impact on results, and how you presented uncertainty.

3.6.7 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Walk through your validation process, stakeholder engagement, and how you documented your decision.

3.6.8 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 the frameworks you used to prioritize, communicate trade-offs, and maintain focus on the core deliverables.

3.6.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Detail the tools, scripts, and monitoring systems you put in place, and the long-term benefits realized.

3.6.10 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe your communication strategy, use of evidence, and how you built consensus across teams.

4.1 Company-specific tips:

Demonstrate a clear understanding of Crexi’s mission to transform the commercial real estate industry through technology. Familiarize yourself with the commercial real estate transaction lifecycle and how data can drive efficiencies in marketing, analysis, and deal closure. Reference Crexi’s platform features, such as dynamic property listings, transaction services, and due diligence tools, and consider how data engineering supports these offerings.

Research Crexi’s scale and impact—millions of monthly users, billions in closed deals, and trillions in marketed property value. Be prepared to discuss how scalable data solutions can empower such high-volume operations and how you would design systems to support rapid growth and reliability in a fast-paced environment.

Understand the business context behind Crexi’s data needs. Show that you appreciate the importance of timely, accurate, and actionable data for stakeholders ranging from brokers to investors. Consider how your work as a Data Engineer directly contributes to improving decision-making and deal velocity in commercial real estate.

4.2 Role-specific tips:

Highlight your experience designing and optimizing scalable ETL pipelines.
Be ready to walk through your approach to building robust data pipelines for ingesting, transforming, and loading large, heterogeneous datasets. Discuss your strategies for handling diverse data sources, schema evolution, and ensuring data quality and reliability at scale.

Demonstrate proficiency in SQL, Python, and cloud data architectures (especially AWS and Snowflake).
Prepare to write performant SQL queries that handle complex joins, aggregations, and filtering on production-scale data. Be comfortable discussing Python code for data manipulation, automation, and pipeline orchestration, and explain your experience deploying data workflows in cloud environments.

Showcase your familiarity with modern data engineering tools such as Airflow, DBT, Kafka, and Spark.
Discuss how you have used these tools for workflow orchestration, real-time data streaming, and large-scale data processing. Explain your approach to monitoring, error handling, and automating pipeline reliability.

Emphasize your approach to data quality, cleaning, and validation.
Prepare examples of how you have profiled messy datasets, implemented automated validation checks, and resolved data inconsistencies. Highlight your ability to prioritize cleaning steps under tight deadlines and communicate data caveats to leadership.

Present your experience with data warehouse design and system architecture.
Describe your process for designing scalable, maintainable data warehouses—discuss schema choices, partitioning strategies, and supporting internationalization, compliance, and rapid business expansion.

Demonstrate your troubleshooting skills for diagnosing and resolving data pipeline failures.
Be ready to outline your workflow for log analysis, root cause identification, and remediation planning. Show how you use monitoring, alerting, and automation to prevent recurrence and maintain system reliability.

Show how you communicate technical concepts and data insights to non-technical stakeholders.
Share examples of tailoring your messaging using visualizations, analogies, and clear recommendations. Describe how you adapt your communication for different audiences and make complex data accessible and actionable for business users.

Prepare to discuss real-world data engineering challenges and trade-offs.
Reflect on projects where you balanced analytical rigor with practical constraints, such as handling missing data, reconciling conflicting metrics from multiple sources, or negotiating scope creep across teams. Illustrate your adaptability, problem-solving, and stakeholder management skills.

Articulate your impact on business outcomes through data engineering.
Be ready to share stories where your work enabled critical insights, improved data reliability, or accelerated decision-making for the organization. Show that you understand the direct link between data engineering excellence and Crexi’s success in the commercial real estate sector.

5.1 How hard is the Crexi Data Engineer interview?
The Crexi Data Engineer interview is challenging and designed to assess both deep technical expertise and business acumen. You’ll be tested on your ability to architect scalable data pipelines, optimize big data workflows, and troubleshoot real-world problems in commercial real estate technology. Candidates who can demonstrate hands-on experience with modern data engineering tools, cloud architectures, and the ability to translate complex data requirements into actionable solutions will stand out.

5.2 How many interview rounds does Crexi have for Data Engineer?
Crexi typically conducts 5–6 interview rounds for Data Engineer candidates. The process includes an application and resume review, recruiter screen, technical/case/skills round, behavioral interview, final onsite or virtual panel interviews, and an offer/negotiation stage. Each round is designed to evaluate different aspects of your technical skills, problem-solving ability, and cultural fit.

5.3 Does Crexi ask for take-home assignments for Data Engineer?
Crexi occasionally includes a take-home assignment or technical assessment, especially in the technical/case/skills round. These assignments may involve designing a scalable data pipeline, writing advanced SQL queries, or solving a real-world scenario relevant to commercial real estate data challenges. The goal is to assess your practical problem-solving skills and approach to data engineering tasks.

5.4 What skills are required for the Crexi Data Engineer?
Key skills for a Crexi Data Engineer include advanced proficiency in SQL and Python, experience with cloud data platforms (especially AWS and Snowflake), expertise in building and optimizing ETL pipelines, and familiarity with tools like Airflow, DBT, Kafka, and Spark. Strong abilities in data warehouse design, data quality assurance, troubleshooting pipeline failures, and communicating technical concepts to non-technical stakeholders are essential. Understanding the commercial real estate industry’s data needs is a significant plus.

5.5 How long does the Crexi Data Engineer hiring process take?
The typical Crexi Data Engineer hiring process takes about 3–4 weeks from application to offer. Each interview round is generally scheduled a week apart, though the timeline may vary based on candidate availability and team priorities. Candidates with highly relevant experience or strong referrals may progress more quickly.

5.6 What types of questions are asked in the Crexi Data Engineer interview?
Expect a mix of technical and behavioral questions. Technical questions focus on designing scalable data pipelines, SQL coding, cloud architecture, data warehouse schema, data quality, and troubleshooting real-world failures. Behavioral questions assess your collaboration skills, communication style, problem-solving approach, and ability to handle ambiguity or scope changes. Scenario-based questions often relate directly to challenges in the commercial real estate data space.

5.7 Does Crexi give feedback after the Data Engineer interview?
Crexi typically provides feedback through its recruiting team. While detailed technical feedback may be limited, you can expect high-level insights on your interview performance and next steps. Candidates are encouraged to follow up for clarification if needed.

5.8 What is the acceptance rate for Crexi Data Engineer applicants?
Crexi Data Engineer roles are competitive, with an estimated acceptance rate of 3–5% for qualified applicants. The company seeks candidates who not only excel technically but also align with its mission and demonstrate an understanding of commercial real estate data challenges.

5.9 Does Crexi hire remote Data Engineer positions?
Yes, Crexi offers remote Data Engineer positions, though some roles may require occasional visits to the office for team collaboration or onboarding. The company supports flexible work arrangements to attract top talent across geographic locations.

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

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