The fund for public health in new york, inc. Data Engineer Interview Guide

Getting ready for a Data Engineer interview at The Fund for Public Health in New York City (FPHNYC)? The FPHNYC Data Engineer interview process typically spans multiple question topics and evaluates skills in areas like scalable data pipeline development, data governance and security, cloud-based data solutions, and effective stakeholder communication. Interview preparation is crucial for this role, as candidates are expected to demonstrate not only technical excellence but also the ability to collaborate across public health, technology, and data science teams to deliver impactful solutions for population health challenges.

In preparing for the interview, you should:

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

The Fund for Public Health in New York City (FPHNYC) is a 501(c)3 non-profit organization dedicated to advancing the health and well-being of all New Yorkers through innovative public health programs and partnerships. Working closely with the NYC Department of Health and Mental Hygiene, FPHNYC incubates and implements initiatives that address pressing health needs, foster private sector support, and educate the public. As a Data Engineer, you will help build and optimize secure, scalable data pipelines that empower timely, equitable, and impactful public health interventions, directly supporting FPHNYC’s mission to improve health outcomes and promote health equity across the city.

As a Data Engineer at The Fund for Public Health in New York, Inc., you will develop, maintain, and optimize secure, scalable data pipelines that support advanced public health analytics and surveillance initiatives. Working within the Center for Population Health Data Science and collaborating with the Department of Health and Mental Hygiene, you will integrate diverse health data sources, implement data governance and security best practices, and support real-time and batch data processing. Your role involves collaborating with public health experts and IT teams to ensure data infrastructure aligns with agency needs, enabling timely and actionable insights to advance public health outcomes across New York City. This position is crucial in strengthening citywide population health surveillance and improving the well-being of New Yorkers.

2.1 Stage 1: Application & Resume Review

The initial review process is conducted by the HR team and technical managers, focusing on your experience with developing and maintaining scalable data pipelines, cloud-based data solutions (such as AWS, Azure, or GCP), and familiarity with public health data standards (HL7, FHIR). Your resume should clearly highlight hands-on expertise in Python or R, big data technologies like Hadoop and Spark, and any experience in health informatics. Emphasize your contributions to secure, efficient data architectures and collaborative projects with cross-functional teams. To prepare, tailor your resume to showcase technical depth, project impact, and alignment with public health objectives.

2.2 Stage 2: Recruiter Screen

A recruiter or HR specialist will reach out for a brief introductory call, typically lasting 20–30 minutes. This conversation assesses your motivation for working in public health, your understanding of FPHNYC’s mission, and high-level alignment with the technical and collaborative aspects of the Data Engineer role. Expect questions about your interest in the organization, your ability to work in hybrid settings, and your eligibility based on location requirements. Prepare by articulating your passion for public health data engineering and your ability to communicate complex technical concepts to diverse audiences.

2.3 Stage 3: Technical/Case/Skills Round

This stage often involves one or two rounds led by data engineering managers or senior engineers and may include live or take-home exercises. You’ll be evaluated on designing secure, scalable data pipelines, optimizing ETL processes, and integrating data from heterogeneous sources. Expect to discuss your experience with cloud platforms, big data frameworks, and system design for real-time and batch data processing. You may be asked to solve practical problems such as debugging pipeline failures, data cleaning, or architecting solutions for public health surveillance. Preparation should include reviewing your recent projects, brushing up on data modeling, and practicing clear explanations of technical decisions.

2.4 Stage 4: Behavioral Interview

A behavioral round with team leads or cross-functional stakeholders focuses on your collaboration skills, adaptability, and communication style. You’ll be asked to describe how you manage stakeholder expectations, mentor junior staff, and navigate challenges in fast-paced, mission-driven environments. The interviewers will assess your ability to communicate technical insights to non-technical audiences, prioritize competing demands, and demonstrate values such as equity, humility, and diplomacy. Prepare by reflecting on concrete examples of teamwork, project management, and public health impact.

2.5 Stage 5: Final/Onsite Round

The final or onsite round is typically conducted by the Executive Director for Data Engineering and Management and other senior leaders. This stage may include a combination of technical deep-dives, system design scenarios, and strategic questions about scaling data infrastructure for citywide public health initiatives. You’ll be expected to demonstrate holistic thinking about data governance, security, and the alignment of technology with public health goals. Preparation should include reviewing the organization’s programs, anticipating questions about real-world data challenges, and formulating thoughtful perspectives on modernizing public health data systems.

2.6 Stage 6: Offer & Negotiation

Once you successfully pass the previous rounds, the HR team will present an offer detailing compensation, benefits, work schedule, and hybrid arrangements. You’ll have the opportunity to discuss the salary range, clarify expectations around emergency response participation, and negotiate terms that fit your needs. Prepare by reviewing industry standards, understanding FPHNYC’s benefits package, and considering your priorities for work-life balance and professional growth.

2.7 Average Timeline

The interview process for Data Engineer roles at FPHNYC typically spans 3–5 weeks from application to offer. Fast-track candidates with highly relevant public health and data engineering experience may complete the process within 2–3 weeks, while standard pace candidates should expect about a week between stages. Scheduling for technical and onsite rounds may vary depending on team availability and agency priorities.

Next, let’s review the types of interview questions you may encounter throughout these stages.

3.1 Data Pipeline Design & ETL

Expect questions focused on building, optimizing, and troubleshooting robust data pipelines. Interviewers will assess your ability to scale ETL processes, handle diverse data sources, and ensure reliability and accuracy in data movement.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Break down the ingestion process, highlight modular architecture, and discuss error handling and data validation. Reference scalable technologies and monitoring best practices.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Outline each stage from raw data ingestion to feature engineering, model serving, and reporting. Emphasize automation, scheduling, and failover strategies.

3.1.3 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Describe schema validation, error handling, incremental loads, and reporting mechanisms. Discuss how to ensure data integrity and handle malformed files.

3.1.4 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Walk through monitoring, logging, root-cause analysis, and rollback strategies. Suggest automation for alerts and recovery.

3.1.5 Let's say that you're in charge of getting payment data into your internal data warehouse.
Describe the ingestion, transformation, and loading steps. Highlight quality checks, reconciliation, and documentation for repeatability.

3.2 Database Design & Data Modeling

This section covers schema design, data organization, and building systems that support analytics and operational needs. Expect to reason about trade-offs, scalability, and normalization.

3.2.1 Design a data warehouse for a new online retailer.
Outline dimensional modeling, fact and dimension tables, and partitioning strategies for performance. Discuss how business requirements shape schema choices.

3.2.2 Design a database for a ride-sharing app.
Identify key entities, relationships, and indexing strategies. Address scalability and real-time analytics considerations.

3.2.3 System design for a digital classroom service.
Break down user roles, access control, and data flow. Explain how you would ensure data security and support reporting.

3.2.4 Write a query to get the current salary for each employee after an ETL error.
Show how to identify and correct inconsistencies using SQL. Discuss reconciliation methods and audit trails.

3.3 Data Cleaning & Quality Assurance

Interviewers want to see your approach to cleaning, validating, and profiling data. You’ll be asked about handling real-world messiness and maintaining high standards for data quality.

3.3.1 Describing a real-world data cleaning and organization project
Explain your process for profiling, cleaning, and documenting messy datasets. Highlight automation and reproducibility.

3.3.2 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Discuss techniques for parsing irregular layouts and normalizing data. Address error checking and transformation logic.

3.3.3 Ensuring data quality within a complex ETL setup
Describe validation checks, monitoring, and alerting for data discrepancies. Suggest strategies for ongoing quality assurance.

3.3.4 How would you approach improving the quality of airline data?
Detail your approach for profiling, cleaning, and documenting issues. Discuss how you’d prioritize fixes and communicate caveats.

3.3.5 Debugging and reconciling inconsistencies in marriage data
Outline your troubleshooting process, including checks for duplicates, nulls, and logical errors. Emphasize reproducibility and transparency.

3.4 Analytics & Data Integration

You’ll be tested on integrating multiple data sources, extracting insights, and supporting decision-making with reliable analytics. Demonstrate your skills in joining, aggregating, and communicating findings.

3.4.1 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?
Walk through profiling, cleaning, joining, and validating disparate data. Emphasize documentation and reproducibility.

3.4.2 Create and write queries for health metrics for stack overflow
Describe how you would define, calculate, and report actionable health metrics. Focus on aggregation, filtering, and visualization.

3.4.3 How to present complex data insights with clarity and adaptability tailored to a specific audience
Explain your approach to tailoring visualizations and explanations for non-technical stakeholders. Emphasize storytelling and actionable recommendations.

3.4.4 Demystifying data for non-technical users through visualization and clear communication
Highlight techniques for simplifying dashboards and reports. Discuss best practices for bridging technical gaps.

3.5 Tooling, Automation & Optimization

Expect questions on choosing the right technologies, automating workflows, and optimizing for performance. Show your ability to balance speed, accuracy, and maintainability.

3.5.1 python-vs-sql
Compare the strengths of each tool for different parts of the pipeline. Justify your choice based on scalability, maintainability, and speed.

3.5.2 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Discuss your selection of open-source technologies, pipeline orchestration, and cost-saving strategies. Address trade-offs and scalability.

3.5.3 Design a data pipeline for hourly user analytics.
Outline architecture for real-time or near-real-time analytics. Emphasize partitioning, aggregation, and efficiency.

3.5.4 Modifying a billion rows
Describe strategies for bulk updates, minimizing downtime, and ensuring data integrity. Reference batching, indexing, and rollback plans.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a project where your analysis directly impacted a business outcome or operational process. Illustrate the problem, your approach, and the measurable result.

3.6.2 Describe a challenging data project and how you handled it.
Choose a project with technical or stakeholder obstacles, and explain your problem-solving process. Emphasize resilience and adaptability.

3.6.3 How do you handle unclear requirements or ambiguity?
Describe your approach to clarifying objectives, iterating on deliverables, and communicating with stakeholders. Highlight your proactive attitude.

3.6.4 Walk us through how you built a quick-and-dirty de-duplication script on an emergency timeline.
Share your strategy for rapid problem-solving under pressure, including prioritizing critical issues and documenting your solution.

3.6.5 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Explain how you leveraged early prototypes to gather feedback, clarify requirements, and drive consensus.

3.6.6 How have you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow?
Discuss your triage process for minimal viable analysis and communicating uncertainty transparently.

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?
Detail your reconciliation process, validation steps, and how you communicated findings to stakeholders.

3.6.8 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, choosing appropriate imputation or exclusion strategies, and communicating limitations.

3.6.9 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe how you adapted your communication style, clarified technical concepts, and built trust.

3.6.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Explain the automation tools or scripts you built, the impact on workflow, and how you ensured ongoing data reliability.

4.1 Company-specific tips:

Familiarize yourself with FPHNYC’s mission and the public health challenges they tackle in New York City. Understand how data engineering supports their initiatives, especially in population health surveillance, disease prevention, and health equity. Review recent projects and partnerships between FPHNYC and the NYC Department of Health and Mental Hygiene, and be ready to discuss how data can drive public health outcomes.

Research public health data standards relevant to FPHNYC, such as HL7 and FHIR, and consider how these standards influence data integration and interoperability in large-scale health systems. Be prepared to discuss how you would handle sensitive health data, ensuring privacy, security, and compliance with regulations like HIPAA.

Get to know the organizational culture of FPHNYC, which values collaboration, equity, and mission-driven work. Prepare to share examples of teamwork, adaptability, and your passion for improving health outcomes through technology. Demonstrate your ability to communicate technical concepts to non-technical audiences, especially stakeholders in public health and policy.

4.2 Role-specific tips:

4.2.1 Master the design and optimization of scalable, secure data pipelines for heterogeneous health data sources.
Practice breaking down the architecture of ETL pipelines that ingest, clean, and transform data from multiple health systems, labs, and public sources. Emphasize modularity, error handling, and automation for reliability. Be ready to discuss strategies for incremental loads, schema validation, and monitoring to ensure data integrity and timeliness.

4.2.2 Demonstrate expertise in cloud-based data solutions and big data frameworks.
Review your experience with cloud platforms such as AWS, Azure, or GCP, and big data technologies like Hadoop and Spark. Prepare to explain how you would leverage these tools for scalable storage, real-time and batch processing, and secure data sharing. Highlight your ability to optimize costs and performance in cloud environments.

4.2.3 Show proficiency in database design, data modeling, and supporting analytics needs.
Practice designing schemas and data warehouses that support both operational reporting and advanced analytics. Discuss your approach to dimensional modeling, partitioning, and indexing for performance and scalability. Be prepared to reason about trade-offs between normalization and denormalization, especially in the context of public health data.

4.2.4 Illustrate your approach to data cleaning, validation, and ongoing quality assurance.
Prepare examples of profiling, cleaning, and documenting messy datasets, especially those with irregular layouts or incomplete records. Emphasize automation and reproducibility in your data quality workflows, and discuss how you would implement validation checks and alerts for discrepancies in complex ETL setups.

4.2.5 Highlight your ability to integrate diverse data sources and deliver actionable insights to stakeholders.
Showcase your skills in joining, aggregating, and analyzing data from disparate sources, such as health records, survey responses, and operational logs. Practice explaining complex findings in clear, accessible terms, and tailor your communication to different stakeholder groups, including public health officials and policy makers.

4.2.6 Demonstrate expertise in tooling, automation, and performance optimization.
Be ready to discuss your choices between tools like Python and SQL for different pipeline tasks, justifying your decisions based on scalability, maintainability, and speed. Share examples of automating recurrent data-quality checks and optimizing workflows for high-volume data processing, such as bulk updates and real-time analytics.

4.2.7 Prepare for behavioral questions that assess collaboration, problem-solving, and adaptability.
Reflect on past experiences where you balanced speed versus rigor, clarified ambiguous requirements, or reconciled conflicting data sources. Practice sharing stories that demonstrate your ability to communicate with stakeholders, mentor junior staff, and adapt to changing priorities in a mission-driven environment.

4.2.8 Be ready to discuss strategies for data governance, privacy, and security in public health contexts.
Review best practices for handling sensitive health information, implementing role-based access controls, and ensuring compliance with HIPAA and other regulations. Prepare to articulate how you would align data infrastructure with organizational policies and ethical standards.

4.2.9 Showcase your commitment to continuous improvement and innovation in public health data engineering.
Share examples of how you’ve automated processes to prevent recurring data issues, piloted new technologies, or contributed to the modernization of data systems. Demonstrate your curiosity, initiative, and drive to deliver impactful solutions for population health.

5.1 How hard is the Fund for Public Health in New York, Inc. Data Engineer interview?
The FPHNYC Data Engineer interview is moderately challenging, especially for those new to public health data systems. You’ll be expected to demonstrate advanced data pipeline design, cloud platform expertise, and a strong grasp of data governance and security. The process also tests your ability to collaborate with diverse stakeholders and communicate technical concepts clearly. Candidates with experience in health informatics or large-scale data integration will find themselves well-prepared.

5.2 How many interview rounds does the Fund for Public Health in New York, Inc. have for Data Engineer?
Typically, there are five to six rounds: application/resume review, recruiter screen, technical/case interviews, behavioral interviews, a final onsite or executive round, and an offer/negotiation stage. Each round focuses on a mix of technical skills, public health knowledge, and collaboration abilities.

5.3 Does the Fund for Public Health in New York, Inc. ask for take-home assignments for Data Engineer?
Yes, candidates are often given practical take-home assignments or live technical exercises. These may involve designing ETL pipelines, solving data integration problems, or demonstrating proficiency with cloud-based data solutions. The assignments are tailored to real-world public health data scenarios.

5.4 What skills are required for the Fund for Public Health in New York, Inc. Data Engineer?
Key skills include scalable data pipeline development, cloud platform expertise (AWS, Azure, GCP), big data technologies (Hadoop, Spark), database design and data modeling, data cleaning and validation, and a solid understanding of public health data standards (HL7, FHIR). Strong communication and stakeholder management abilities are also essential, as you’ll be collaborating across technical and public health teams.

5.5 How long does the Fund for Public Health in New York, Inc. Data Engineer hiring process take?
The process typically takes 3–5 weeks from application to offer. Fast-track candidates with highly relevant experience may move through in 2–3 weeks, while most applicants should expect about a week between interview stages, depending on team schedules and agency priorities.

5.6 What types of questions are asked in the Fund for Public Health in New York, Inc. Data Engineer interview?
You’ll encounter technical questions on ETL pipeline design, data modeling, cloud platform usage, and data cleaning. Expect scenario-based questions on integrating heterogeneous health data, ensuring data quality, and optimizing for performance. Behavioral questions focus on teamwork, adaptability, and communication with non-technical stakeholders. You may also be asked about data governance, privacy, and compliance in public health contexts.

5.7 Does the Fund for Public Health in New York, Inc. give feedback after the Data Engineer interview?
FPHNYC typically provides high-level feedback through HR or recruiters. While detailed technical feedback may be limited, you can expect to hear about your strengths and areas for improvement, especially if you progress to later stages.

5.8 What is the acceptance rate for Fund for Public Health in New York, Inc. Data Engineer applicants?
While specific rates aren’t published, the Data Engineer role at FPHNYC is competitive due to its impact and visibility. The estimated acceptance rate is 3–6% for well-qualified applicants with strong technical and public health backgrounds.

5.9 Does the Fund for Public Health in New York, Inc. hire remote Data Engineer positions?
FPHNYC offers hybrid work arrangements for Data Engineers, with a mix of remote and on-site collaboration. Some roles may require periodic office visits or in-person meetings, especially for projects involving sensitive data or cross-functional teamwork. Be sure to clarify remote work expectations during the interview process.

• Related guides:
  • The Fund for Public Health in New York, Inc. Data Engineer Interview Questions
  • How to Prepare for a Data Engineer Interview (Updated in 2025)
  • Data Engineer Case Study Interview Questions + Guide in 2025
  • Top 12 SQL Interview Questions for Data Engineers in 2025
  • Data Engineer Career Path: Skills, Salary, and Growth Opportunities in 2025

Ready to ace your The Fund for Public Health in New York, Inc. Data Engineer interview? It’s not just about knowing the technical skills—you need to think like a FPHNYC Data Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at The Fund for Public Health in New York, Inc. and similar organizations.

With resources like the The Fund for Public Health in New York, Inc. Data Engineer Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!