Research Foundation Of The City University Of New York Data Engineer Interview Guide

Getting ready for a Data Engineer interview at the Research Foundation of The City University Of New York? The Research Foundation of CUNY Data Engineer interview process typically spans multiple question topics and evaluates skills in areas like data pipeline design, ETL processes, data cleaning, system architecture, and communicating insights to diverse audiences. Interview preparation is especially important for this role, as candidates are expected to demonstrate expertise in building robust data systems, ensuring data integrity, and translating complex datasets into actionable solutions that support educational and research initiatives.

In preparing for the interview, you should:

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

The Research Foundation of The City University of New York (RFCUNY) is a nonprofit organization that supports CUNY’s research and sponsored programs by providing administrative, fiscal, and technical services. RFCUNY facilitates grant management, compliance, and financial operations for researchers and academic projects across the CUNY system. As a Data Engineer, you will play a crucial role in building and maintaining data infrastructure that supports effective decision-making and operational efficiency, directly contributing to RFCUNY’s mission of advancing research and scholarship within the university community.

As a Data Engineer at the Research Foundation of The City University Of New York, you are responsible for designing, building, and maintaining data pipelines that support research and administrative initiatives across the university system. You will work closely with researchers, analysts, and IT teams to ensure reliable data integration, storage, and access, enabling accurate and efficient analysis of large and complex datasets. Key tasks include developing ETL processes, optimizing database performance, and ensuring data quality and security. This role is vital in supporting evidence-based decision-making and advancing research by providing robust data infrastructure tailored to the needs of academic projects and institutional operations.

2.1 Stage 1: Application & Resume Review

The process begins with an initial screening of your application materials, where the focus is on your experience with data engineering fundamentals such as ETL pipeline development, data warehouse design, SQL proficiency, and experience with large-scale data cleaning and transformation. The review also considers your background in integrating data from multiple sources and familiarity with tools or platforms relevant to education or research environments. Attention to detail in your resume—highlighting technical projects, system design, and communication of data insights—is crucial at this stage.

2.2 Stage 2: Recruiter Screen

A recruiter or HR representative will typically reach out for a brief phone or Zoom call to discuss your background, motivations for applying, and alignment with the organization’s mission. This stage may include clarifying your technical experience, such as working with unstructured data, building robust data pipelines, and your approach to communicating complex data to non-technical audiences. Preparation should center on articulating your career interests, key technical skills, and ability to collaborate across teams.

2.3 Stage 3: Technical/Case/Skills Round

This round is often conducted via Zoom and may involve both live and asynchronous components, such as responding to technical questions sent in advance. You can expect in-depth assessment of your problem-solving skills in areas like designing scalable ETL and reporting pipelines, handling data quality issues, transforming and aggregating large datasets, and system design for data warehouses or digital classroom solutions. Demonstrating proficiency in SQL, Python, and data modeling, as well as your ability to optimize data workflows for analytics and reporting, is essential. Preparation should include reviewing past data engineering projects, practicing system design, and being ready to discuss approaches to real-world data cleaning and pipeline failures.

2.4 Stage 4: Behavioral Interview

The behavioral interview explores your communication style, teamwork, and adaptability in complex, cross-functional environments. Interviewers will probe for examples of how you’ve demystified data for non-technical users, presented insights to diverse audiences, and navigated challenges in multi-stakeholder settings. They may also assess your motivation for joining the organization and your fit with its research-focused culture. To prepare, reflect on scenarios where you’ve driven collaboration, clarified technical concepts, and adapted your approach for different stakeholders.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves a panel interview or a series of discussions with key team members, such as data engineering leads, analytics managers, or IT directors. This round may revisit technical and behavioral themes, diving deeper into your experience with large-scale data systems, troubleshooting pipeline issues, and designing end-to-end solutions for educational or research data needs. Expect scenario-based questions that test your ability to synthesize requirements, prioritize solutions under constraints, and ensure data accessibility and quality. Preparation should include ready examples of past projects, decision-making processes, and strategies for continuous improvement.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive an offer outlining compensation, benefits, and assignment details, often followed by a discussion with HR or the hiring manager. This stage provides an opportunity to clarify expectations, negotiate terms, and understand the onboarding process. Be prepared to discuss your preferred start date, any logistical considerations, and your long-term career goals within the organization.

2.7 Average Timeline

The interview process at the Research Foundation Of The City University Of New York for Data Engineer roles typically spans 2–4 weeks from initial application to offer. Fast-track candidates may complete the process in as little as 1–2 weeks, especially if there’s an urgent project need, while standard pacing allows a few days between each stage for scheduling and deliberation. Virtual interviews and asynchronous technical assessments help streamline the process, but timelines may vary based on team and candidate availability.

Next, let’s dive into the types of interview questions you can expect during each stage of the process.

3.1 Data Pipeline Design & ETL

Expect questions focused on designing, troubleshooting, and optimizing data pipelines and ETL processes. Demonstrate your ability to architect scalable systems, diagnose failures, and ensure reliable data flow across diverse sources.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Describe how you would architect the pipeline to handle varying data formats, ensure data integrity, and scale with increasing volume. Emphasize modularity, 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 the full pipeline from raw ingestion to model serving, highlighting choices for batch vs. streaming, storage solutions, and data transformation steps.

3.1.3 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Explain how you would handle large file uploads, validate and parse data efficiently, and ensure reporting accuracy. Discuss strategies for error handling and schema evolution.

3.1.4 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Walk through your troubleshooting process, including logging, alerting, root cause analysis, and prevention strategies. Stress the importance of documentation and communication.

3.1.5 Let's say that you're in charge of getting payment data into your internal data warehouse.
Detail your approach for reliable ingestion, data validation, and security considerations. Highlight how you would automate processes and monitor for data quality issues.

3.2 Data Modeling & Warehousing

These questions assess your understanding of designing databases and data warehouses to support analytics, scalability, and business operations. Be ready to discuss schema design, normalization, and trade-offs.

3.2.1 Design a data warehouse for a new online retailer
Present your approach to modeling sales, inventory, and customer data. Discuss star vs. snowflake schema and how you’d support evolving business needs.

3.2.2 How would you design a data warehouse for an e-commerce company looking to expand internationally?
Explain considerations for localization, currency conversion, and regulatory compliance. Highlight strategies for partitioning and indexing.

3.2.3 Design a database for a ride-sharing app.
Describe your schema, including tables for users, rides, payments, and ratings. Discuss how you’d optimize for common queries and scalability.

3.2.4 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
List the tools you would select, justify your choices, and describe how you’d balance cost, reliability, and performance.

3.3 Data Cleaning & Quality

You'll encounter questions about handling messy, incomplete, or inconsistent data. Show your expertise in profiling, cleaning, and validating datasets to ensure trustworthy outputs.

3.3.1 Describing a real-world data cleaning and organization project
Share your process for identifying issues, applying cleaning techniques, and documenting changes. Discuss tools used and lessons learned.

3.3.2 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Explain how you would reformat and clean the data for reliable analysis, including handling nulls and inconsistent formats.

3.3.3 How would you approach improving the quality of airline data?
Describe data profiling, anomaly detection, and remediation strategies. Highlight collaboration with business stakeholders.

3.3.4 Ensuring data quality within a complex ETL setup
Discuss techniques for validating data across systems, monitoring for discrepancies, and automating quality checks.

3.4 System Design & Scalability

These questions probe your ability to design systems that scale and adapt to changing requirements. Expect to discuss architectural trade-offs, performance optimization, and future-proofing.

3.4.1 System design for a digital classroom service.
Outline your approach to supporting real-time data, user management, and analytics. Address scalability and reliability.

3.4.2 Modifying a billion rows
Describe efficient strategies for bulk updates, minimizing downtime, and ensuring data consistency.

3.4.3 Aggregating and collecting unstructured data.
Explain your approach to ingesting, storing, and organizing unstructured formats for downstream analytics.

3.4.4 Redesign batch ingestion to real-time streaming for financial transactions.
Discuss architectural changes, latency considerations, and technology choices for real-time processing.

3.5 Data Integration & Analytics

Expect questions about integrating multiple data sources and generating actionable insights. Show how you clean, join, and analyze data to support business goals.

3.5.1 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Describe your process for data profiling, harmonization, and analysis. Emphasize collaboration and iterative improvement.

3.5.2 Demystifying data for non-technical users through visualization and clear communication
Share techniques for making complex data accessible, including visualization, storytelling, and tailored communication.

3.5.3 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss strategies for audience analysis, visualization, and adapting your message for maximum impact.

3.5.4 Making data-driven insights actionable for those without technical expertise
Explain how you translate analytics into practical recommendations and ensure stakeholder buy-in.

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 or operational outcome. Example: "I analyzed usage logs to identify a bottleneck and recommended a system upgrade that reduced downtime by 30%."

3.6.2 Describe a challenging data project and how you handled it.
Share the context, obstacles, and the steps you took to resolve issues. Example: "During a migration, I coordinated cross-functional teams to resolve schema mismatches and ensure data integrity."

3.6.3 How do you handle unclear requirements or ambiguity?
Discuss your approach to clarifying needs, iterative prototyping, and stakeholder communication. Example: "I scheduled frequent check-ins and built wireframes to align expectations before finalizing the pipeline design."

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?
Describe how you facilitated open dialogue and used data to find common ground. Example: "I shared performance metrics from a pilot and encouraged feedback, leading to consensus on the chosen ETL tool."

3.6.5 Walk us through how you built a quick-and-dirty de-duplication script on an emergency timeline.
Highlight your prioritization of must-fix issues and rapid prototyping. Example: "I wrote a Python script using fuzzy matching and shared the results for immediate validation, meeting the urgent reporting deadline."

3.6.6 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Explain your validation process and stakeholder engagement. Example: "I traced data lineage and consulted system owners, ultimately choosing the source with audit logs and documented data transformations."

3.6.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Share your approach to scripting, scheduling, and monitoring. Example: "I implemented automated anomaly detection in our ETL pipeline, reducing manual intervention and recurring errors."

3.6.8 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Discuss your use of project management tools and prioritization frameworks. Example: "I use Kanban boards and weekly planning sessions to align tasks with business impact and ensure timely delivery."

3.6.9 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 assessment of missingness and communication of uncertainty. Example: "I performed MCAR analysis, imputed missing values, and clearly flagged confidence intervals in my report."

3.6.10 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Explain your iterative approach to stakeholder alignment. Example: "I built interactive dashboards with mock data, allowing teams to visualize outcomes and converge on requirements before development."

4.1 Company-specific tips:

Become familiar with the mission and structure of the Research Foundation of the City University Of New York (RFCUNY). Understand how RFCUNY supports research and academic projects across the CUNY system, and be ready to discuss how strong data infrastructure can empower grant management, compliance, and operational efficiency in a nonprofit educational environment.

Research recent RFCUNY initiatives and the types of research and academic programs they support. Demonstrate genuine interest in contributing to the university’s research mission and show awareness of the unique challenges faced by educational institutions in managing diverse and sensitive datasets.

Prepare to discuss how you would approach collaborating with researchers, administrators, and IT teams. Highlight your ability to communicate technical concepts to non-technical stakeholders and your experience adapting solutions to the needs of academic projects.

4.2 Role-specific tips:

4.2.1 Master the end-to-end design of scalable ETL pipelines for heterogeneous data sources.
Practice articulating your approach to building ETL pipelines that can ingest, validate, and transform data from diverse sources—such as student records, grant applications, and external research datasets. Be ready to discuss modular pipeline design, error handling strategies, and monitoring solutions that ensure data reliability and scalability as academic projects grow.

4.2.2 Demonstrate expertise in data cleaning and quality assurance for real-world, messy datasets.
Showcase your experience profiling, cleaning, and validating large datasets, especially those with inconsistent formats or missing values. Prepare to walk through examples where you identified data quality issues, applied targeted cleaning techniques, and built automated checks to prevent recurring problems. Emphasize your ability to document processes and communicate changes to stakeholders.

4.2.3 Highlight your skills in data modeling and data warehouse design tailored to academic and research needs.
Be prepared to discuss schema design decisions, normalization strategies, and trade-offs between star and snowflake schemas. Explain how you would model complex relationships—such as grants, publications, and student outcomes—to support robust analytics and reporting for university stakeholders.

4.2.4 Prepare to troubleshoot and optimize data systems for performance and reliability.
Practice explaining how you diagnose and resolve failures in data pipelines, including root cause analysis, logging, and alerting. Share examples of optimizing database queries, minimizing downtime during bulk updates, and redesigning systems for real-time or batch processing as project requirements evolve.

4.2.5 Show your ability to aggregate and integrate data from multiple sources for actionable insights.
Discuss your process for harmonizing data from payment systems, academic records, and external research partners. Emphasize your approach to joining, cleaning, and analyzing data to generate insights that support decision-making in research and administration.

4.2.6 Illustrate how you communicate complex data insights to non-technical users.
Prepare examples of how you’ve used visualization, clear storytelling, and tailored presentations to make data accessible to researchers, administrators, and grant managers. Focus on your ability to translate technical findings into actionable recommendations and ensure stakeholder buy-in.

4.2.7 Be ready to discuss automation and continuous improvement in data engineering workflows.
Share stories of automating recurrent data-quality checks, scheduling ETL jobs, and building monitoring dashboards. Highlight how your proactive approach reduces manual intervention, prevents errors, and supports the long-term reliability of data systems.

4.2.8 Reflect on your experience working in cross-functional teams and navigating ambiguity.
Prepare to share how you clarify requirements, iterate on prototypes, and align diverse stakeholders around project goals. Emphasize your adaptability and commitment to RFCUNY’s collaborative, research-driven culture.

4.2.9 Practice explaining trade-offs and decision-making in challenging data scenarios.
Be ready to discuss situations where you delivered insights despite incomplete data, made analytical trade-offs, or resolved conflicting metrics across systems. Focus on your ability to assess uncertainty, communicate limitations, and guide teams toward informed decisions.

4.2.10 Prepare examples of building and adapting data infrastructure for evolving academic and research needs.
Discuss how you’ve designed systems to support new research initiatives, integrated emerging data sources, and future-proofed infrastructure for scalability and compliance. Show your enthusiasm for continuous learning and innovation in support of RFCUNY’s mission.

5.1 How hard is the Research Foundation Of The City University Of New York Data Engineer interview?
The interview is moderately challenging, with a strong focus on practical data engineering scenarios relevant to academic and research settings. Candidates are expected to demonstrate proficiency in designing scalable ETL pipelines, data cleaning for complex datasets, and communicating insights to non-technical stakeholders. Familiarity with educational data systems and a collaborative mindset are key differentiators.

5.2 How many interview rounds does Research Foundation Of The City University Of New York have for Data Engineer?
Typically, there are 4–6 rounds, including an application review, recruiter screen, technical/case round, behavioral interview, and a final onsite or panel interview. Some candidates may also encounter asynchronous technical assessments or project-based questions.

5.3 Does Research Foundation Of The City University Of New York ask for take-home assignments for Data Engineer?
Yes, it’s common for candidates to receive a take-home technical assignment or case study. These assignments often focus on designing ETL pipelines, cleaning real-world data, or proposing solutions to academic data integration challenges.

5.4 What skills are required for the Research Foundation Of The City University Of New York Data Engineer?
Key skills include SQL, Python, ETL pipeline development, data modeling, data warehouse design, data cleaning and validation, system architecture, and the ability to communicate technical concepts to diverse audiences. Experience with educational data systems or nonprofit environments is a plus.

5.5 How long does the Research Foundation Of The City University Of New York Data Engineer hiring process take?
The process typically spans 2–4 weeks from initial application to offer. Fast-track candidates may complete it in as little as 1–2 weeks, while standard pacing allows time for scheduling and thorough evaluation at each stage.

5.6 What types of questions are asked in the Research Foundation Of The City University Of New York Data Engineer interview?
Expect a mix of technical and behavioral questions. Technical questions cover ETL pipeline design, data cleaning, data modeling, system architecture, and troubleshooting. Behavioral questions focus on collaboration, communication, navigating ambiguity, and aligning with the organization’s mission.

5.7 Does Research Foundation Of The City University Of New York give feedback after the Data Engineer interview?
Feedback is typically provided through recruiters, especially for candidates who reach the later stages. While detailed technical feedback may be limited, you can expect general insights into your performance and fit for the role.

5.8 What is the acceptance rate for Research Foundation Of The City University Of New York Data Engineer applicants?
While exact rates are not public, the role is competitive due to the organization’s impact and mission-driven culture. An estimated 5–8% of qualified applicants progress to the offer stage.

5.9 Does Research Foundation Of The City University Of New York hire remote Data Engineer positions?
Yes, remote positions are available for Data Engineers, though some roles may require occasional onsite collaboration or presence for key meetings, especially for projects involving sensitive academic data. Flexibility and adaptability to hybrid work environments are valued.

• Related guides:
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  • Data Engineer interview guide
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