Black knight Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Black Knight? The Black Knight Data Engineer interview process typically spans a wide range of question topics and evaluates skills in areas like data pipeline design, ETL development, large-scale data transformation, and stakeholder communication. Interview preparation is especially important for this role at Black Knight, as candidates are expected to architect, implement, and optimize data solutions that support complex financial and business systems, while ensuring data integrity and scalability in a fast-paced environment.

In preparing for the interview, you should:

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

Black Knight is a leading provider of integrated software, data, and analytics solutions for the mortgage and real estate industries. The company delivers technology-driven products and services that help clients manage the entire mortgage lifecycle, from origination and servicing to default and secondary marketing. With a focus on innovation, compliance, and operational efficiency, Black Knight serves banks, lenders, servicers, and other financial institutions across the United States. As a Data Engineer, you will contribute to building and optimizing data pipelines and infrastructure that support the company’s mission to deliver actionable insights and superior customer experiences.

As a Data Engineer at Black Knight, you will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support the company’s mortgage and financial technology solutions. You will collaborate with data scientists, analysts, and software engineers to ensure the efficient movement and transformation of large datasets, enabling accurate analytics and reporting. Core tasks include data modeling, ETL development, data integration from multiple sources, and ensuring data quality and integrity. This role is essential for powering data-driven decision-making and delivering reliable solutions that support Black Knight’s clients in the mortgage and real estate industries.

2.1 Stage 1: Application & Resume Review

This initial stage involves a thorough assessment of your application materials by Black Knight’s talent acquisition team. They focus on your experience with building and maintaining data pipelines, expertise in ETL processes, familiarity with data warehousing, and your ability to work with large-scale datasets. Strong emphasis is placed on demonstrated technical proficiency in SQL, Python, and data engineering best practices, as well as your experience in designing scalable, reliable data solutions. To prepare, ensure your resume clearly highlights relevant projects, quantifies your impact, and aligns your skills with the core requirements of a data engineering role.

2.2 Stage 2: Recruiter Screen

A recruiter will conduct a 20–30 minute phone or video call to discuss your background, interest in Black Knight, and alignment with the company’s mission and values. Expect questions about your career trajectory, motivation for applying, and general understanding of data engineering within the financial technology sector. Preparation should focus on articulating your professional story, your passion for data-driven problem-solving, and your familiarity with Black Knight’s products or industry.

2.3 Stage 3: Technical/Case/Skills Round

This stage is typically led by a senior data engineer or technical lead and centers on evaluating your hands-on ability to solve real-world data engineering challenges. You may be given live coding exercises, system design problems, or case studies involving data pipeline architecture, data cleaning, and large-scale data processing. Expect to demonstrate proficiency in SQL (such as writing queries to count transactions or aggregate data), Python (for data manipulation and pipeline automation), and knowledge of ETL workflows, data warehousing, and streaming solutions. Preparation should include practicing coding without external libraries, designing robust data pipelines, and explaining your problem-solving approach clearly.

2.4 Stage 4: Behavioral Interview

The behavioral round is designed to assess your communication skills, teamwork, and approach to overcoming challenges in data projects. Interviewers may include future colleagues, managers, or cross-functional partners. You’ll be asked to describe past experiences with data cleaning, resolving data quality issues, stakeholder communication, and presenting data insights to non-technical audiences. Emphasize your adaptability, collaboration, and ability to make complex technical concepts accessible. Prepare by reflecting on specific projects where you navigated hurdles, drove measurable results, and contributed to a positive team dynamic.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of a series of in-depth interviews (virtual or onsite) with multiple stakeholders, such as data engineering managers, analytics directors, and potential team members. This round may blend technical deep-dives, system design whiteboarding, and scenario-based questions on topics like real-time data streaming, pipeline failure diagnosis, and designing scalable solutions for financial data. You may also be asked to critique or improve existing data processes and discuss your approach to ensuring data integrity and reliability. Preparation should focus on your ability to synthesize technical and business requirements, think strategically, and demonstrate leadership within data engineering projects.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive an offer from Black Knight’s HR or recruiting team. This stage includes a review of compensation, benefits, and start date, as well as any questions about the role or company culture. Be prepared to discuss your expectations and negotiate based on your experience and the value you bring to the team.

2.7 Average Timeline

The typical Black Knight Data Engineer interview process spans 3–5 weeks from application to offer. Fast-track candidates may complete the process in as little as two weeks, especially if there is urgency to fill the role or if you have highly relevant experience. Standard pacing involves about one week between each stage, with some flexibility depending on team schedules and candidate availability.

Next, let’s dive into the types of interview questions you can expect throughout these stages.

3.1 Data Engineering System Design

System design questions for data engineers at Black Knight often focus on building robust, scalable, and efficient data pipelines. You should be ready to discuss architecture choices, trade-offs in data modeling, and approaches to ensure reliability and maintainability.

3.1.1 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Describe key architectural components, error handling, and scalability considerations. Emphasize strategies for data validation, schema evolution, and automation.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes
Lay out the ETL process, storage solutions, and how you’d enable downstream analytics or machine learning. Highlight monitoring, alerting, and data quality checkpoints.

3.1.3 Design a data warehouse for a new online retailer
Discuss schema design (star/snowflake), partitioning, and how you’d support both transactional and analytical workloads. Address scalability, data governance, and real-time vs. batch ingestion.

3.1.4 Redesign batch ingestion to real-time streaming for financial transactions
Compare streaming technologies, discuss latency vs. throughput trade-offs, and explain how you’d ensure data consistency and reliability.

3.1.5 Design a data pipeline for hourly user analytics
Describe how you’d aggregate user data, manage time windows, and optimize for both performance and cost. Consider how to backfill or reprocess historical data.

3.2 Data Quality & Troubleshooting

Black Knight values engineers who can ensure high data quality and quickly resolve pipeline failures. Expect questions about diagnosing, preventing, and remediating data integrity issues.

3.2.1 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Walk through root cause analysis, logging, monitoring, and recovery strategies. Highlight automation and documentation for recurring issues.

3.2.2 Ensuring data quality within a complex ETL setup
Describe validation frameworks, automated tests, and how you’d handle schema drift or upstream data changes.

3.2.3 How would you approach improving the quality of airline data?
Explain profiling, anomaly detection, and feedback loops to catch and remediate quality issues. Discuss collaboration with data producers and consumers.

3.2.4 Describing a real-world data cleaning and organization project
Share your approach to profiling, cleaning, and documenting messy datasets. Highlight reproducibility and communication with stakeholders.

3.2.5 Describing a data project and its challenges
Focus on how you identified and overcame bottlenecks, technical hurdles, or misaligned requirements. Emphasize your role in ensuring project success.

3.3 SQL & Data Manipulation

These questions assess your ability to efficiently query, aggregate, and transform large datasets—skills essential for data engineers at Black Knight.

3.3.1 Write a SQL query to count transactions filtered by several criterias.
Demonstrate filtering, aggregation, and handling of edge cases like nulls or missing data. Optimize for performance on large tables.

3.3.2 Write a function that splits the data into two lists, one for training and one for testing.
Outline logic for reproducible random splits, ensuring no data leakage, and handling edge cases with small or imbalanced datasets.

3.3.3 Write a function to calculate precision and recall metrics.
Explain your approach for computing metrics from confusion matrix components, and clarify how you’d handle class imbalance.

3.3.4 Write a function to return the names and ids for ids that we haven't scraped yet.
Describe efficient set operations, indexing strategies, and how you’d scale this for large datasets.

3.4 Data Communication & Stakeholder Collaboration

Data engineers at Black Knight must translate complex technical concepts for business stakeholders and drive data-driven decisions across teams.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss tailoring depth, visuals, and narrative to stakeholder needs. Emphasize actionable recommendations and transparency about limitations.

3.4.2 Making data-driven insights actionable for those without technical expertise
Share strategies for simplifying concepts, using analogies, and focusing on business impact rather than technical jargon.

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Highlight techniques for effective dashboard design, user training, and iterative feedback to improve accessibility.

3.4.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Describe frameworks for gathering requirements, managing scope, and maintaining open communication throughout the project lifecycle.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Describe a specific scenario where your analysis influenced a business or technical outcome. Focus on your methodology, the impact, and how you communicated your findings.

3.5.2 Describe a challenging data project and how you handled it.
Share a project with significant technical or organizational obstacles, detailing your problem-solving approach and the results.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your process for gathering additional information, clarifying goals, and iterating with stakeholders to ensure alignment.

3.5.4 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?
Highlight your communication skills, openness to feedback, and how you reached consensus or compromise.

3.5.5 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Discuss your negotiation and facilitation skills, and the analytical framework you used to standardize metrics.

3.5.6 You’re given a dataset that’s full of duplicates, null values, and inconsistent formatting. The deadline is soon, but leadership wants insights from this data for tomorrow’s decision-making meeting. What do you do?
Detail your triage process for rapid data cleaning, prioritizing high-impact fixes, and communicating limitations to stakeholders.

3.5.7 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 for root cause analysis, validation, and how you aligned stakeholders on the resolution.

3.5.8 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Share your experience building automated tests, monitoring, or alerting to proactively catch and resolve data issues.

3.5.9 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss your method for handling missing data, communicating uncertainty, and ensuring the insights were still actionable.

3.5.10 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Explain your prioritization framework, communication strategy, and how you maintained focus on core objectives.

4.1 Company-specific tips:

Familiarize yourself with Black Knight’s position in the mortgage and real estate technology sector. Understand how data engineering drives solutions for mortgage lifecycle management, compliance, and operational efficiency. Review recent product launches, acquisitions, and major initiatives to demonstrate your awareness of the company’s strategic direction.

Learn the types of data Black Knight works with—mortgage origination, servicing, default, and secondary marketing datasets. Research common data challenges in the financial industry, such as regulatory compliance, privacy, and data integration across legacy systems.

Understand Black Knight’s commitment to innovation and reliability. Be prepared to discuss how robust data engineering practices can enable accurate reporting, actionable analytics, and superior customer experiences for banks, lenders, and servicers.

4.2 Role-specific tips:

Prepare to design and discuss scalable, fault-tolerant data pipelines.
Practice explaining your approach to architecting end-to-end data pipelines that reliably ingest, transform, and deliver data for financial applications. Focus on how you’d handle schema evolution, error handling, and automation for regular processes like nightly ETL jobs or real-time streaming ingestion.

Demonstrate expertise in ETL development and data transformation.
Be ready to walk through real-world examples of ETL workflows you’ve built or optimized, especially those involving large, messy datasets. Highlight your strategies for data cleaning, deduplication, and ensuring consistency across multiple data sources.

Showcase your SQL and Python skills for large-scale data manipulation.
Expect hands-on coding exercises involving complex SQL queries, aggregation, and filtering. Practice writing efficient code for data splitting, metric calculation (such as precision and recall), and set operations, all while considering performance on large tables.

Discuss your experience with data warehousing and modeling.
Review concepts like star and snowflake schema design, partitioning, and supporting both transactional and analytical workloads. Be prepared to explain how you optimize storage, manage real-time vs. batch data flows, and ensure scalability for growing datasets.

Explain your approach to data quality and troubleshooting.
Prepare to describe how you diagnose pipeline failures, implement monitoring and alerting, and automate data-quality checks. Share specific examples of root cause analysis, documentation, and collaboration with stakeholders to resolve recurring issues.

Highlight your ability to communicate insights and collaborate with stakeholders.
Practice translating complex technical concepts into clear, actionable recommendations for non-technical audiences. Be ready to discuss your strategies for presenting data insights, managing misaligned expectations, and driving consensus across teams.

Reflect on behavioral scenarios involving ambiguity, conflict, and tight deadlines.
Think through detailed stories from your experience where you handled unclear requirements, negotiated project scope, or delivered results under pressure. Use these examples to showcase your adaptability, problem-solving skills, and leadership in data engineering projects.

Emphasize your commitment to data integrity and compliance.
Given Black Knight’s focus on financial technology, be ready to discuss how you ensure data privacy, regulatory compliance, and accuracy in all data engineering work. Mention any experience with sensitive or regulated datasets, and your process for maintaining high standards.

Prepare to critique and improve existing data processes.
Expect scenario-based questions where you’re asked to identify bottlenecks, propose optimizations, or redesign legacy systems. Show your ability to synthesize technical and business requirements and think strategically about long-term solutions.

Demonstrate your ability to automate and scale data solutions.
Share examples of automating repetitive tasks, building reusable components, and scaling pipelines to handle increasing data volumes. Highlight your focus on reliability, maintainability, and proactive monitoring to prevent future issues.

5.1 How hard is the Black Knight Data Engineer interview?
The Black Knight Data Engineer interview is considered moderately to highly challenging, especially for candidates without prior experience in financial services or large-scale data infrastructure. The process emphasizes practical skills in designing and optimizing data pipelines, ETL development, and troubleshooting real-world data quality issues. Candidates who are comfortable with SQL, Python, and communicating complex technical concepts to stakeholders will find themselves well-prepared. Expect a rigorous assessment of both technical depth and business acumen.

5.2 How many interview rounds does Black Knight have for Data Engineer?
Typically, there are 5 to 6 rounds in the Black Knight Data Engineer interview process. These include an initial application and resume review, a recruiter screen, one or two technical/case rounds, a behavioral interview, and a final onsite or virtual interview with multiple team members. Each round targets a specific set of skills, from coding and system design to stakeholder communication and problem-solving.

5.3 Does Black Knight ask for take-home assignments for Data Engineer?
Take-home assignments are occasionally part of the Black Knight Data Engineer interview, especially when the team wants to evaluate your approach to real-world data pipeline challenges or ETL tasks. These assignments usually involve designing or coding a data solution, cleaning a messy dataset, or troubleshooting a simulated pipeline failure. Clear communication of your methodology and results is just as important as technical accuracy.

5.4 What skills are required for the Black Knight Data Engineer?
Key skills for the Black Knight Data Engineer role include advanced SQL, Python programming, ETL development, data modeling, and experience with data warehousing and real-time streaming solutions. You should be adept at designing scalable data pipelines, troubleshooting data quality issues, and collaborating with stakeholders to deliver actionable insights. Familiarity with financial data, regulatory compliance, and large-scale data integration is a distinct advantage.

5.5 How long does the Black Knight Data Engineer hiring process take?
The typical timeline for the Black Knight Data Engineer hiring process is 3 to 5 weeks from application to offer. Fast-track candidates may progress in as little as two weeks, while standard pacing allows about a week between each stage. Delays may occur based on scheduling, candidate availability, or team workload, but Black Knight is known for maintaining a transparent and efficient process.

5.6 What types of questions are asked in the Black Knight Data Engineer interview?
Expect a mix of technical, system design, and behavioral questions. Technical rounds cover SQL coding, Python-based data manipulation, ETL workflows, and data warehousing. System design questions focus on building scalable pipelines, handling real-time streaming, and ensuring data integrity. Behavioral interviews assess your communication, teamwork, and ability to navigate ambiguity or conflict in data projects. You may also encounter scenario-based questions about troubleshooting pipeline failures and collaborating with business stakeholders.

5.7 Does Black Knight give feedback after the Data Engineer interview?
Black Knight typically provides high-level feedback through recruiters, especially after final rounds. While detailed technical feedback may be limited, you can expect a summary of strengths and areas for improvement. If you’re not selected, constructive feedback is often shared to help you understand the decision and guide future applications.

5.8 What is the acceptance rate for Black Knight Data Engineer applicants?
While exact numbers aren’t public, the Data Engineer role at Black Knight is highly competitive, with an estimated acceptance rate of 3–6% for qualified applicants. Success depends on strong technical skills, relevant industry experience, and the ability to communicate and collaborate effectively with cross-functional teams.

5.9 Does Black Knight hire remote Data Engineer positions?
Yes, Black Knight does offer remote Data Engineer positions, though some roles may require occasional onsite presence for team meetings or collaboration. Flexibility varies by team and project needs, but remote work is increasingly supported for candidates who demonstrate strong self-management and communication skills.

• Related guides:
  • Black Knight interview questions
  • Data Engineer interview guide
  • “Top Data Engineer interview tips”

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