Fis Data Engineer Interview Guide

Getting ready for a Data Engineer interview at FIS? The FIS Data Engineer interview process typically spans several question topics and evaluates skills in areas like ETL pipeline design, data modeling, financial data analysis, and stakeholder communication. Interview preparation is especially important for this role at FIS, as candidates are expected to demonstrate expertise in building robust data infrastructure, optimizing data flows for financial operations, and translating complex business requirements into scalable technical solutions. FIS values data engineers who can drive innovation in fintech, ensure data quality, and collaborate effectively across diverse teams to deliver actionable insights and operational efficiency.

In preparing for the interview, you should:

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

FIS is a global leader in financial technology, powering commerce and financial transactions for businesses and communities in over 130 countries. Serving more than 20,000 clients and one million merchant locations, FIS advances the way the world pays, banks, and invests by providing innovative fintech solutions. The company’s mission centers on enabling businesses to thrive through secure and efficient financial services, with a strong focus on inclusivity and technological advancement. As a Data Engineer, you will play a vital role in building data infrastructure that supports financial operations and empowers data-driven decision-making, directly contributing to FIS’s commitment to transforming the financial industry.

As a Data Engineer at FIS, you will design, build, and maintain robust data pipelines that support financial settlement and reconciliation processes across the company’s fintech platform. You will generate actionable reports by analyzing financial data and key performance indicators, providing critical insights to finance, operations, and engineering teams. Your responsibilities include developing efficient ETL solutions, ensuring data quality, optimizing operational workflows, and translating business requirements into scalable, well-documented code. You will collaborate cross-functionally to drive data-driven decision-making, participate in code reviews, and provide on-call support for production issues. This role is essential in enabling FIS to deliver innovative, compliant financial products and services.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume by the FIS talent acquisition team. They look for strong experience in building and maintaining data pipelines, financial data analysis, ETL (Extract, Transform, Load) processes, and collaboration with cross-functional teams—especially within fintech or financial operations. Highlighting your technical expertise in scalable data pipeline design, experience with large datasets, and the ability to translate business requirements into robust data solutions will help you stand out. Ensure your resume clearly demonstrates your impact on operational efficiency, data quality, and process improvement in prior roles.

2.2 Stage 2: Recruiter Screen

Next, a recruiter will reach out for a 30 to 45-minute phone conversation. This discussion centers on your motivation for joining FIS, your background in data engineering, and your fit with the company’s culture and mission. You should be prepared to articulate your experience working with financial data, collaborating with finance and operations teams, and your approach to problem-solving in a fast-paced environment. Demonstrating enthusiasm for fintech innovation and the ability to communicate complex technical concepts to non-technical stakeholders will be advantageous.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or more interviews focused on your technical proficiency. Expect a blend of live coding exercises, system design questions, and case studies relevant to data engineering in the fintech domain. You may be asked to design scalable ETL pipelines, troubleshoot data transformation failures, build reporting solutions for financial data, or optimize batch ingestion to real-time streaming for transaction data. Interviewers—often senior engineers or technical leads—will evaluate your SQL and Python skills, your approach to data cleaning and aggregation, and your ability to design robust, maintainable data systems. Practice articulating your methodology for handling unstructured data, integrating diverse data sources, and ensuring data quality and security in enterprise environments.

2.4 Stage 4: Behavioral Interview

The behavioral interview, typically conducted by a hiring manager or cross-functional team member, explores your collaboration, leadership, and communication skills. You’ll be expected to discuss past experiences leading data projects, overcoming challenges in data-driven environments, and working with stakeholders from finance, operations, and engineering. Scenarios may involve presenting complex data insights to non-technical audiences, navigating ambiguity, or mentoring junior engineers. Focus on providing clear, structured responses that showcase your adaptability, problem-solving mindset, and ability to drive projects from concept to production.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of a virtual or onsite panel interview with multiple team members, including senior engineers, data architects, and managers from finance and operations. This round may combine additional technical deep-dives (such as designing a data warehouse for a new product or diagnosing recurring pipeline failures), collaborative case studies, and further behavioral questions. You may also be asked to walk through a real-world data project, explain your decision-making process, and demonstrate how you ensure operational excellence and data-driven decision-making at scale. The panel will be assessing both your technical depth and your alignment with FIS’s collaborative, innovative culture.

2.6 Stage 6: Offer & Negotiation

If you are successful through the previous rounds, the recruiter will extend a formal offer and discuss details including compensation, benefits, and start date. You’ll have the opportunity to negotiate within the company’s published pay range, with consideration given to your experience, location, and the specific needs of the team. Be prepared to discuss your expectations and any questions about the FIS work environment, growth opportunities, and team structure.

2.7 Average Timeline

The FIS Data Engineer interview process typically spans 3 to 5 weeks from application to offer, depending on candidate availability and scheduling logistics. Fast-track candidates with highly relevant experience and immediate availability may complete the process in as little as 2 weeks, while the standard pace involves about a week between each stage. The technical and onsite rounds may be condensed into a single day or spread over multiple days based on panel availability and your preferences.

Next, let’s break down the types of interview questions you can expect at each stage of the FIS Data Engineer process.

3.1. Data Engineering & ETL Design

Data engineering interviews at Fis focus heavily on your ability to design, implement, and optimize robust data pipelines and ETL processes. Expect questions that probe your understanding of scalable architectures, data warehousing, and the handling of real-world data challenges in high-volume environments.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe your approach to integrating multiple data sources with varying formats, ensuring data consistency, and building a pipeline that can scale with increased data volume. Highlight your experience with orchestration tools and error handling.

3.1.2 Redesign batch ingestion to real-time streaming for financial transactions.
Explain your strategy for transitioning from batch to real-time data processing, including technology choices, data latency considerations, and system reliability.

3.1.3 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Detail the steps from data ingestion to model serving, including data cleaning, feature engineering, and monitoring. Emphasize modular design and scalability.

3.1.4 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Discuss methods to handle large, potentially messy CSV files, including validation, error handling, and performance optimizations for both storage and retrieval.

3.1.5 Let's say that you're in charge of getting payment data into your internal data warehouse.
Outline the architecture and steps you would use to ensure secure, accurate, and timely ingestion of payment data, mentioning data validation, schema evolution, and monitoring.

3.1.6 Design a data warehouse for a new online retailer.
Describe your approach to data modeling, schema design, and how you would ensure efficient query performance and scalability as the retailer grows.

3.1.7 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Explain how you would select and integrate open-source tools for ETL, storage, and reporting, considering cost, reliability, and maintainability.

3.2. Data Quality, Cleaning & Troubleshooting

Data engineers at Fis are expected to ensure high data quality and resolve issues quickly. You’ll be tested on your experience with data cleaning, pipeline failures, and strategies for maintaining data integrity across multiple systems.

3.2.1 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Lay out your troubleshooting process, including logging, alerting, root cause analysis, and long-term fixes to prevent recurrence.

3.2.2 Ensuring data quality within a complex ETL setup
Discuss frameworks and tools you use to monitor, validate, and enforce data quality across multiple ETL stages and sources.

3.2.3 Describing a real-world data cleaning and organization project
Share a specific example of a data cleaning project, the challenges faced, and the outcome. Focus on tools, techniques, and the impact on downstream analytics.

3.2.4 Modifying a billion rows
Describe your approach to updating massive datasets efficiently and safely, including batching, indexing, and minimizing downtime.

3.3. SQL & Data Analysis

SQL proficiency is fundamental for data engineers at Fis. Be prepared for questions that assess your ability to write complex queries, aggregate large datasets, and extract actionable insights from transactional data.

3.3.1 Write a SQL query to count transactions filtered by several criterias.
Demonstrate your ability to filter, aggregate, and optimize queries for performance, especially on large transaction tables.

3.3.2 Write a query to compute the average time it takes for each user to respond to the previous system message
Showcase your understanding of window functions and time-difference calculations, ensuring accuracy even with missing or out-of-order data.

3.3.3 Write a query to get the current salary for each employee after an ETL error.
Explain how you would identify and correct discrepancies caused by ETL failures, ensuring data consistency and auditability.

3.4. System Design & Scalability

System design questions at Fis assess your ability to architect solutions that handle scale, complexity, and evolving business needs. You’ll be expected to discuss both high-level and implementation details.

3.4.1 System design for a digital classroom service.
Outline your approach to designing a scalable, reliable system for handling user data, content, and analytics in an educational platform.

3.4.2 Design a solution to store and query raw data from Kafka on a daily basis.
Describe your architecture for ingesting, storing, and efficiently querying high-volume streaming data, including partitioning and schema evolution.

3.4.3 Aggregating and collecting unstructured data.
Discuss strategies for handling unstructured or semi-structured data, including parsing, normalization, and storage choices.

3.5. Communication, Stakeholder Management & Data Accessibility

Fis values data engineers who can bridge the gap between technical teams and business stakeholders. You’ll be asked to explain complex topics clearly and make data accessible to non-technical audiences.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your approach to tailoring presentations, using visualizations and narratives that resonate with different audiences.

3.5.2 Demystifying data for non-technical users through visualization and clear communication
Share strategies for making technical results actionable, such as dashboard design, storytelling, and simplifying jargon.

3.5.3 Making data-driven insights actionable for those without technical expertise
Explain how you translate complex findings into concrete recommendations, using analogies or examples that fit the business context.

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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 led directly to a business outcome, emphasizing the metrics you tracked and the impact of your recommendation.

3.6.2 Describe a challenging data project and how you handled it.
Choose a project with multiple obstacles (technical or organizational), outlining your problem-solving process and what you learned.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying goals, communicating with stakeholders, and iterating quickly to reduce uncertainty.

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?
Share how you fostered collaboration, listened to feedback, and adjusted your solution or communication style to build consensus.

3.6.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Discuss how you quantified new requests, communicated trade-offs, and maintained a structured prioritization process.

3.6.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Outline how you communicated risks, proposed phased delivery, and ensured transparency throughout the project.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Provide an example where you used evidence, storytelling, and stakeholder alignment to drive adoption of your insights.

3.6.8 Describe a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Explain your approach to handling missing data, communicating uncertainty, and ensuring the reliability of your conclusions.

3.6.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Highlight a specific automation you built, the problem it solved, and the long-term benefits for your team or organization.

3.6.10 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Describe your prioritization framework, time management strategies, and any tools or processes you use to track progress.

4.1 Company-specific tips:

Immerse yourself in FIS’s mission and the unique challenges of the financial technology sector. Understand how FIS leverages data engineering to power global payments, banking, and investment solutions, and be ready to discuss how your skills can drive innovation and operational excellence in a regulated fintech environment.

Research recent FIS product launches, strategic acquisitions, and technology initiatives. Highlight how these developments impact data infrastructure and analytics, and be prepared to reference them when discussing your approach to solving business problems.

Familiarize yourself with the types of financial data FIS processes, such as payment transactions, merchant settlements, and compliance reporting. Think about how the scale, velocity, and sensitivity of this data present both technical and business challenges for data engineers.

Consider the importance of cross-functional collaboration at FIS. Be ready to give examples of how you’ve worked with finance, operations, and engineering teams to translate business requirements into technical solutions, and how you ensure your work aligns with FIS’s values of security, efficiency, and inclusivity.

4.2 Role-specific tips:

4.2.1 Be ready to design and explain scalable ETL pipelines for financial data.
Practice articulating your approach to building ETL solutions that handle heterogeneous data sources, such as payment, settlement, and transaction records. Emphasize how you ensure data consistency, scalability, and fault tolerance, especially in high-volume, mission-critical environments.

4.2.2 Demonstrate expertise in transitioning batch ingestion to real-time streaming.
Prepare to discuss the technologies and architectural patterns you use for real-time data processing, such as message queues and stream processing frameworks. Highlight how you address challenges like data latency, reliability, and monitoring in the context of financial transactions.

4.2.3 Show your skill in designing robust data pipelines for messy or large datasets.
Give examples of how you handle ingestion, parsing, validation, and storage of unstructured or semi-structured financial data (e.g., CSVs, logs). Detail your strategies for error handling, performance optimization, and maintaining data quality at scale.

4.2.4 Illustrate your approach to secure and accurate ingestion of sensitive payment data.
Discuss your experience with data validation, schema evolution, and monitoring for pipelines that process confidential financial information. Explain how you ensure compliance with security standards and maintain auditability throughout your data workflows.

4.2.5 Be prepared to model and design scalable data warehouses for new financial products.
Talk through your process for data modeling, schema design, and optimizing query performance in data warehouses that support growing business needs. Reference your experience balancing normalization, denormalization, and indexing for both analytics and operational efficiency.

4.2.6 Share your experience with open-source ETL and reporting tools under budget constraints.
Explain how you select and integrate open-source solutions for data engineering tasks, considering reliability, maintainability, and cost. Give examples of how you’ve delivered production-ready reporting pipelines using these tools.

4.2.7 Demonstrate your troubleshooting process for recurring pipeline failures.
Outline your systematic approach to diagnosing and resolving issues, including logging, alerting, root cause analysis, and implementing long-term fixes. Emphasize your commitment to operational excellence and minimizing downtime.

4.2.8 Highlight your strategies for ensuring data quality and integrity.
Describe frameworks, monitoring tools, and validation techniques you use to enforce data quality across complex ETL processes. Share how you proactively identify and resolve data anomalies before they impact downstream analytics.

4.2.9 Provide examples of efficient data cleaning and organization projects.
Discuss a real-world data cleaning challenge you faced, the tools and techniques you used, and the impact your work had on business outcomes. Focus on your ability to turn messy data into actionable insights.

4.2.10 Exhibit your proficiency in SQL for complex financial data analysis.
Prepare to write and explain queries that filter, aggregate, and optimize performance on large transaction tables. Showcase your knowledge of window functions, time-difference calculations, and troubleshooting ETL-related discrepancies in SQL.

4.2.11 Articulate your approach to system design and scalability.
Practice discussing both high-level architecture and detailed implementation for systems that handle scale, complexity, and evolving business needs. Reference your experience with partitioning, schema evolution, and optimizing for query performance.

4.2.12 Show how you make data accessible and actionable for non-technical stakeholders.
Share your methods for presenting complex data insights, using clear visualizations, narratives, and simplified explanations tailored to different audiences. Give examples of how you translate technical findings into concrete business recommendations.

4.2.13 Prepare for behavioral scenarios focused on collaboration, adaptability, and influencing others.
Reflect on experiences where you led data projects, handled ambiguity, negotiated scope, and influenced stakeholders without formal authority. Structure your stories to highlight your communication skills, problem-solving mindset, and ability to drive consensus in cross-functional teams.

5.1 “How hard is the Fis Data Engineer interview?”
The Fis Data Engineer interview is considered moderately to highly challenging, especially for those without prior experience in fintech or large-scale data engineering. The process rigorously tests your ability to design scalable ETL pipelines, handle complex financial datasets, and troubleshoot real-world data issues. Success requires technical depth in data modeling, SQL, and Python, as well as strong communication and problem-solving skills to work effectively across diverse teams.

5.2 “How many interview rounds does Fis have for Data Engineer?”
Fis typically conducts 4 to 6 interview rounds for Data Engineer candidates. The process includes an initial application and resume review, a recruiter phone screen, technical interviews (covering coding, systems design, and case studies), a behavioral interview, and a final onsite or panel round. Each stage is designed to assess both your technical expertise and your ability to collaborate in a cross-functional fintech environment.

5.3 “Does Fis ask for take-home assignments for Data Engineer?”
Yes, Fis often includes a take-home assignment or technical case study as part of the Data Engineer interview process. These assignments focus on designing ETL pipelines, analyzing financial data, or troubleshooting data quality issues. They give you the opportunity to demonstrate your technical skills, attention to detail, and ability to translate business requirements into robust data solutions.

5.4 “What skills are required for the Fis Data Engineer?”
Key skills for a Fis Data Engineer include advanced SQL, Python (or similar programming languages), ETL pipeline design, and data modeling. You should have experience with large-scale data processing, financial data analysis, and data warehousing. Familiarity with orchestration tools, real-time streaming, data quality frameworks, and secure handling of sensitive information is highly valued. Strong communication and stakeholder management skills are also essential for success at Fis.

5.5 “How long does the Fis Data Engineer hiring process take?”
The Fis Data Engineer hiring process generally takes 3 to 5 weeks from application to offer. The timeline can vary depending on candidate availability, scheduling logistics, and the number of interview rounds. Fast-track candidates may complete the process in as little as 2 weeks, while standard timelines allow for about a week between each stage.

5.6 “What types of questions are asked in the Fis Data Engineer interview?”
Expect a mix of technical and behavioral questions. Technical questions cover ETL pipeline design, data modeling, SQL coding, troubleshooting data quality issues, and system design for scalability. You may also be asked to present solutions for real-world financial data scenarios and to explain your approach to optimizing data workflows. Behavioral questions assess your collaboration, adaptability, and ability to communicate complex ideas to both technical and non-technical stakeholders.

5.7 “Does Fis give feedback after the Data Engineer interview?”
Fis typically provides high-level feedback through recruiters after the interview process. While detailed technical feedback may be limited, recruiters often share insights into your performance and areas for improvement, especially if you reach the later stages of the process.

5.8 “What is the acceptance rate for Fis Data Engineer applicants?”
While Fis does not publish specific acceptance rates, the Data Engineer role is competitive, with an estimated acceptance rate of 3-6% for qualified applicants. Candidates with strong fintech experience, robust technical skills, and a proven ability to deliver business value through data engineering have the best chances of success.

5.9 “Does Fis hire remote Data Engineer positions?”
Yes, Fis offers remote Data Engineer positions, depending on the team and project requirements. Some roles may be fully remote, while others could require occasional travel or in-person collaboration at Fis offices. Flexibility in work arrangements is increasingly common, especially for candidates with specialized skills and strong self-management abilities.

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

Ready to ace your Fis Data Engineer interview? It’s not just about knowing the technical skills—you need to think like a Fis 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 Fis and similar companies.

With resources like the Fis 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!