Novus Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Novus? The Novus Data Engineer interview process typically spans multiple question topics and evaluates skills in areas like data pipeline design, ETL development, data quality, and scalable system architecture. Interview preparation is especially important for this role at Novus, as candidates are expected to demonstrate not only technical expertise in building and maintaining robust data infrastructure, but also the ability to communicate insights and solutions to diverse stakeholders and adapt quickly to evolving business needs.

In preparing for the interview, you should:

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

Novus is a technology-driven company specializing in advanced data solutions that empower organizations to harness the full potential of their information assets. Operating at the intersection of data engineering and analytics, Novus delivers scalable infrastructure and innovative tools to support data-driven decision-making across various industries. As a Data Engineer at Novus, you will play a key role in designing, building, and maintaining robust data pipelines that are essential for delivering actionable insights and supporting the company’s mission to transform complex data into strategic value for clients.

As a Data Engineer at Novus, you are responsible for designing, building, and maintaining scalable data pipelines that enable the company to collect, process, and analyze large volumes of data efficiently. You will collaborate with data scientists, analysts, and software engineers to ensure data is accurate, accessible, and well-structured for various business needs. Key tasks include developing ETL processes, optimizing data storage solutions, and implementing best practices for data quality and security. This role is integral to supporting Novus’s data-driven initiatives, empowering teams with reliable data infrastructure to drive informed decision-making and innovation.

2.1 Stage 1: Application & Resume Review

The process begins with an in-depth review of your application and resume, focusing on your experience with data engineering fundamentals, large-scale data pipelines, ETL processes, data warehousing, and your ability to work with both structured and unstructured data. Novus looks for evidence of technical proficiency in Python, SQL, and modern data architecture, as well as your capacity to communicate complex data concepts to both technical and non-technical stakeholders. To prepare, ensure your resume highlights quantifiable impacts on past data projects, experience with cloud platforms, and examples of collaborating across teams.

2.2 Stage 2: Recruiter Screen

In this stage, a recruiter will conduct a 30–45 minute phone or video call to discuss your background, motivations for joining Novus, and your familiarity with the company’s tech stack and data challenges. Expect high-level questions about your career trajectory, interest in data engineering, and ability to adapt to Novus’s fast-paced, collaborative environment. Preparation should include a concise summary of your experience, reasons for pursuing data engineering at Novus, and readiness to discuss your technical and soft skills.

2.3 Stage 3: Technical/Case/Skills Round

This round is typically conducted by a data engineering team member or technical lead and involves a combination of technical assessments and case-based discussions. You may encounter live coding challenges in SQL and Python, system design questions (such as designing robust ETL pipelines, scalable data warehouses, or real-time streaming solutions), and scenario-based problem solving (like troubleshooting pipeline failures or optimizing data transformation workflows). You should be ready to articulate your approach to data modeling, pipeline reliability, data quality, and handling large volumes of data. Preparation should emphasize hands-on practice with data pipeline design, real-world data cleaning, and system architecture diagrams.

2.4 Stage 4: Behavioral Interview

The behavioral round is usually led by a hiring manager or cross-functional partner and explores your collaboration skills, adaptability, and approach to overcoming challenges in data projects. You’ll be asked to reflect on past experiences—such as navigating hurdles in complex data initiatives, communicating insights to non-technical audiences, and ensuring data accessibility. Novus values clear communication and the ability to demystify technical concepts, so be prepared with examples that demonstrate these strengths. Practicing STAR-format stories about data project challenges, team dynamics, and stakeholder management is recommended.

2.5 Stage 5: Final/Onsite Round

The final or onsite round often consists of multiple interviews with various team members, including senior engineers, product managers, and data leaders. This stage may involve a deep-dive technical interview (such as designing a data platform for a new product line), whiteboarding system architecture, and advanced scenario-based questions (like evaluating the impact of a data-driven promotion or diagnosing repeated pipeline failures). You may also be asked to present a previous data project or solution to a mixed technical/non-technical panel. Success here depends on your ability to synthesize technical depth with business acumen and stakeholder communication. Preparation should include reviewing your portfolio, practicing technical presentations, and anticipating cross-functional questions.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll move to the offer and negotiation stage, where the recruiter will discuss compensation, benefits, and start date. This is your opportunity to clarify any remaining questions about the role, team structure, and career development at Novus. Preparation involves researching industry compensation benchmarks and having clear priorities for negotiation.

2.7 Average Timeline

The typical Novus Data Engineer interview process spans approximately 3–5 weeks from initial application to offer. Candidates with highly relevant experience or internal referrals may move through the process more quickly, sometimes in under three weeks, while the standard pace allows about a week between each interview stage. Take-home or technical assessments, if included, usually have a 3–5 day deadline, and scheduling for onsite rounds depends on team and candidate availability.

Below, you'll find a breakdown of the most relevant interview questions asked throughout the Novus Data Engineer process to help you prepare for each stage.

3.1 Data Pipeline Design & ETL

For Novus Data Engineer roles, expect deep dives into designing and optimizing data pipelines, scalable ETL architectures, and real-time streaming solutions. You should focus on demonstrating your ability to build robust, maintainable systems that efficiently ingest, transform, and serve data across diverse use cases. Be ready to discuss trade-offs between batch and streaming, open-source tool selection, and handling heterogeneous data sources.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Explain how you would architect a flexible pipeline that can handle multiple data formats, ensure data quality, and scale with increasing partner integrations. Highlight your approach to schema evolution, error handling, and monitoring.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Describe the steps for ingesting raw data, cleaning and transforming it, storing it efficiently, and enabling downstream analytics or machine learning. Emphasize modularity, reliability, and automation.

3.1.3 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Discuss how you would ensure data integrity, handle malformed files, and provide timely reporting. Include considerations for parallel processing, validation, and error notifications.

3.1.4 Redesign batch ingestion to real-time streaming for financial transactions.
Outline the architecture changes needed to support real-time data flows, focusing on latency, fault tolerance, and consistency. Address how you would monitor, scale, and recover from failures.

3.1.5 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Detail your selection of open-source technologies, orchestration strategies, and methods for ensuring scalability and maintainability. Justify your choices with respect to cost and future growth.

3.2 Data Modeling & System Architecture

This category tests your ability to design data warehouses, optimize schemas, and build systems that support high transaction volumes and complex analytics. Novus values engineers who can balance normalization, performance, and business requirements while anticipating future scaling needs.

3.2.1 Design a data warehouse for a new online retailer.
Describe your approach to modeling sales, inventory, and customer data, supporting both operational and analytical queries. Discuss partitioning, indexing, and handling slowly changing dimensions.

3.2.2 System design for a digital classroom service.
Explain how you would architect a system to support real-time interactions, data storage, and analytics for a digital classroom. Highlight scalability, security, and integration with external services.

3.2.3 Designing a pipeline for ingesting media to built-in search within LinkedIn.
Discuss strategies for indexing, metadata extraction, and supporting fast search queries over large volumes of media data. Address challenges in scaling and maintaining search accuracy.

3.2.4 Determine the requirements for designing a database system to store payment APIs.
Outline schema design, transaction handling, and security considerations for a high-throughput payment system. Emphasize reliability and auditability.

3.3 Data Cleaning & Quality Assurance

Novus expects Data Engineers to proactively address data quality issues, automate cleaning processes, and ensure reliability in analytics outputs. You should be able to profile, diagnose, and remediate issues in large, messy datasets, while communicating uncertainty and trade-offs.

3.3.1 Describing a real-world data cleaning and organization project.
Share your approach to identifying and resolving inconsistencies, duplicates, and nulls in a production dataset. Highlight automation, reproducibility, and documentation.

3.3.2 How would you approach improving the quality of airline data?
Detail your process for profiling, cleaning, and validating data from multiple sources. Discuss collaboration with stakeholders and ongoing monitoring.

3.3.3 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Explain how you would standardize and clean complex data formats to enable reliable analysis. Focus on automation and error detection.

3.3.4 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Describe your troubleshooting workflow, root cause analysis, and remediation steps. Emphasize monitoring and prevention strategies.

3.3.5 Ensuring data quality within a complex ETL setup.
Discuss techniques for validating data across multiple sources, handling schema drift, and maintaining consistency in reporting.

3.4 Analytics, Reporting & Communication

Data Engineers at Novus often bridge technical and business teams, making it essential to present insights clearly and tailor communication to diverse audiences. You should be able to design dashboards, communicate uncertainty, and make data accessible for decision-making.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience.
Describe your strategies for simplifying technical findings and adapting presentations for executives, product managers, or operations.

3.4.2 Demystifying data for non-technical users through visualization and clear communication.
Share how you use visualization tools, plain language, and stakeholder engagement to ensure data is actionable.

3.4.3 Making data-driven insights actionable for those without technical expertise.
Explain your approach to translating findings into recommendations, using analogies or business context.

3.4.4 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time.
Discuss dashboard design principles, real-time data integration, and tailoring metrics for operational use.

3.5 Data Engineering Tools & Technology Choices

This section focuses on your ability to select and justify tools, languages, and frameworks for data engineering tasks. Novus values engineers who can weigh trade-offs and align technology choices with business needs.

3.5.1 python-vs-sql
Compare the strengths and weaknesses of Python and SQL for different data engineering scenarios. Discuss factors influencing your choice, such as scalability, maintainability, and team skills.

3.5.2 Modifying a billion rows
Describe techniques for efficiently updating massive datasets, including bulk operations, partitioning, and minimizing downtime.

3.5.3 Design a data pipeline for hourly user analytics.
Explain your approach to aggregating, storing, and serving user activity data on an hourly basis. Focus on scalability, latency, and automation.

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3.6 Behavioral Questions

3.6.1 Tell Me About a Time You Used Data to Make a Decision
Share a specific project where your analysis directly impacted business strategy, product development, or operational efficiency. Focus on the recommendation, communication, and measurable outcome.

3.6.2 Describe a Challenging Data Project and How You Handled It
Discuss a complex project with technical or organizational hurdles. Highlight your problem-solving approach, collaboration, and lessons learned.

3.6.3 How Do You Handle Unclear Requirements or Ambiguity?
Explain your method for clarifying objectives, engaging stakeholders, and iteratively refining deliverables in ambiguous situations.

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 fostered collaboration, presented evidence, and navigated differing opinions to reach 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?
Share your process for quantifying requests, prioritizing needs, and communicating trade-offs to stakeholders.

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?
Detail how you balanced transparency, incremental delivery, and risk mitigation to manage expectations.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation
Explain how you built trust, communicated impact, and persuaded decision-makers to act on your analysis.

3.6.8 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 data reconciliation, validation, and stakeholder engagement in resolving discrepancies.

3.6.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again
Discuss your strategy for building automation, monitoring, and documentation to ensure sustained data quality.

3.6.10 Describe a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Share how you assessed missingness, chose appropriate imputation or exclusion methods, and communicated uncertainty in your findings.

4.1 Company-specific tips:

Familiarize yourself with Novus’s mission to empower organizations through advanced data solutions. Understand how Novus operates at the intersection of data engineering and analytics, and be prepared to discuss how your skills align with their goal of transforming complex data into strategic value for clients. Research recent Novus initiatives, products, and client success stories to demonstrate your genuine interest and awareness of their business impact.

Learn about Novus’s technology stack, including their preferred cloud platforms, data storage solutions, and open-source tools. Be ready to discuss how you would leverage these technologies to design scalable infrastructure and support data-driven decision-making. Highlight any experience you have with similar stacks and draw parallels to Novus’s environment.

Prepare to articulate how you would collaborate with Novus’s diverse teams, including data scientists, analysts, and product managers. Novus values clear communication and stakeholder engagement, so reflect on examples where you bridged technical and non-technical gaps, enabling others to make informed decisions based on data.

4.2 Role-specific tips:

4.2.1 Master designing scalable and flexible ETL pipelines for heterogeneous data sources.
Practice explaining how you would architect ETL pipelines that can ingest, transform, and validate data from multiple formats and sources. Emphasize your approach to schema evolution, error handling, and monitoring for reliability. Be ready to discuss trade-offs between batch and streaming architectures, and how you would scale pipelines as partner integrations grow.

4.2.2 Be ready to discuss end-to-end data pipeline design for analytics and machine learning use cases.
Articulate the steps involved in ingesting raw data, cleaning and transforming it, optimizing storage, and enabling downstream analytics. Highlight your experience with modular pipeline components, automation, and reliability. Use examples to show how your designs support both operational reporting and predictive modeling.

4.2.3 Demonstrate your expertise in data cleaning, profiling, and quality assurance at scale.
Share specific approaches to identifying and resolving inconsistencies, duplicates, and nulls within large datasets. Discuss automation strategies, reproducibility, and documentation practices that ensure sustained data quality. Be prepared to present examples of diagnosing and remediating pipeline failures, as well as techniques for validating data across multiple sources.

4.2.4 Highlight your ability to design and optimize data models and system architecture for high-volume use cases.
Explain your process for modeling data warehouses, optimizing schemas, and balancing normalization with performance. Discuss how you would design systems to handle complex analytics, real-time interactions, and high transaction volumes. Reference partitioning, indexing, slowly changing dimensions, and security considerations in your answers.

4.2.5 Showcase your technology selection skills and ability to justify tool choices.
Prepare to compare the strengths of languages like Python and SQL for different data engineering tasks. Discuss how you evaluate open-source tools, cloud platforms, and orchestration frameworks based on scalability, maintainability, and cost. Use examples from past projects to illustrate your decision-making process and alignment with business needs.

4.2.6 Practice translating technical insights into actionable recommendations for non-technical audiences.
Develop clear strategies for presenting complex data findings to executives, product managers, or operations teams. Use visualization, plain language, and business analogies to make insights accessible. Be ready to discuss how you tailor communication to different stakeholders and ensure data-driven decisions are understood and adopted.

4.2.7 Prepare real-world examples of troubleshooting and optimizing large-scale data pipelines.
Be ready to walk through your workflow for diagnosing repeated failures in nightly transformations, root cause analysis, and implementing monitoring or prevention strategies. Highlight your experience with bulk operations, partitioning, and minimizing downtime when updating massive datasets.

4.2.8 Reflect on behavioral competencies that Novus values in Data Engineers.
Prepare STAR-format stories about collaborating across teams, handling ambiguous requirements, influencing stakeholders, and managing scope creep. Emphasize your adaptability, communication skills, and ability to deliver critical insights—even when working with incomplete or messy data.

4.2.9 Show your commitment to automation and sustainable data quality practices.
Discuss your experience building automated data-quality checks, monitoring systems, and documentation to prevent recurring issues. Use examples to demonstrate how you’ve improved reliability and enabled teams to focus on innovation rather than firefighting data problems.

4.2.10 Demonstrate your analytical trade-offs and decision-making under uncertainty.
Be prepared to explain how you assess missingness in datasets, choose appropriate imputation or exclusion methods, and communicate uncertainty in your findings. Show that you can deliver actionable insights even when data is imperfect, balancing rigor with practicality.

5.1 How hard is the Novus Data Engineer interview?
The Novus Data Engineer interview is considered challenging, with a strong emphasis on technical depth, real-world problem-solving, and communication skills. You’ll be expected to design scalable data pipelines, troubleshoot complex ETL issues, and demonstrate a clear understanding of data modeling and quality assurance. Candidates who excel at both technical execution and stakeholder engagement are best positioned to succeed.

5.2 How many interview rounds does Novus have for Data Engineer?
Novus typically conducts 5–6 interview rounds for Data Engineer roles. The process includes an application and resume review, a recruiter screen, technical/case/skills interviews, a behavioral round, and a final onsite or virtual panel with multiple team members. Each round evaluates different aspects of your technical and collaborative abilities.

5.3 Does Novus ask for take-home assignments for Data Engineer?
Yes, Novus often includes a take-home technical assignment or case study in the process. These assignments usually focus on designing or troubleshooting data pipelines, ETL processes, or data quality scenarios, and candidates are given 3–5 days to complete them.

5.4 What skills are required for the Novus Data Engineer?
Key skills for Novus Data Engineers include expertise in Python and SQL, designing and building scalable ETL pipelines, data modeling, data warehousing, cloud platforms (such as AWS, GCP, or Azure), and automating data quality checks. Strong communication skills and the ability to translate technical insights for non-technical audiences are also highly valued.

5.5 How long does the Novus Data Engineer hiring process take?
The typical Novus Data Engineer hiring process spans 3–5 weeks from initial application to offer. Timelines can vary depending on candidate availability, scheduling for technical assessments, and onsite interviews. Candidates with highly relevant experience or internal referrals may progress faster.

5.6 What types of questions are asked in the Novus Data Engineer interview?
Expect a mix of technical, case-based, and behavioral questions. Technical questions cover data pipeline design, ETL development, data modeling, system architecture, and troubleshooting data quality issues. Behavioral questions assess collaboration, communication, adaptability, and stakeholder management.

5.7 Does Novus give feedback after the Data Engineer interview?
Novus generally provides feedback through recruiters, especially after technical or onsite rounds. While feedback may be high-level, it’s intended to help candidates understand their strengths and areas for improvement.

5.8 What is the acceptance rate for Novus Data Engineer applicants?
The acceptance rate for Novus Data Engineer roles is competitive, with an estimated 3–6% of applicants receiving offers. Novus looks for candidates with demonstrated technical expertise, problem-solving ability, and strong collaboration skills.

5.9 Does Novus hire remote Data Engineer positions?
Yes, Novus offers remote Data Engineer positions, with some roles requiring occasional office visits for team collaboration or project kickoffs. Remote flexibility depends on team needs and project requirements.

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

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