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Rubric for ETL Questions

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Understanding how your responses to ETL questions will be evaluated is crucial. This lesson provides insight into the key areas that interviewers assess, what they're looking for in strong candidates, and how you can prepare to excel in these areas.

Key evaluation themes

Interviewers typically assess candidates across five main themes when evaluating responses to ETL questions:

  1. Problem understanding and requirements gathering
  2. Technical design and architectural approach
  3. Performance and scalability considerations
  4. Communication and articulation
  5. Adaptability and iteration

Rubric for ETL Pipeline Design Interviews

Let's explore each of these themes in detail, including what constitutes a strong response, common pitfalls to avoid, and a rubric that illustrates different levels of performance.

1. Problem understanding and requirements gathering

Interviewers want to see how well you grasp the business context and technical requirements of an ETL problem. Some interviewers/companies may refer to this as functional and nonfunctional requirement gathering.

What a strong response looks like:

  • You ask relevant questions covering both business and technical aspects
  • You inquire about primary business processes, expected data volume, growth rate, and specific throughput or latency performance requirements for the overall system
  • You demonstrate understanding by asking insightful questions that reveal hidden complexities

Common pitfalls:

  • Failing to ask clarifying questions
  • Asking only surface-level questions that miss critical aspects of the problem
  • Immediately jumping into ETL design without fully understanding the requirements

Rubric:

LevelCriteriaExample
Very WeakFails to ask clarifying questions or misunderstands key requirementsCandidate immediately starts designing architecture without seeking any clarification on the business process or data requirements
WeakAsks surface-level questions and misses critical aspects of the problemCandidate asks about data volume but fails to inquire about batch/real-time processes and/or types of data (structured, semi-structured, or unstructured)
StrongAsks relevant questions covering both business and technical aspectsCandidate inquires about primary business use cases, expected data volume, growth rate, and specific performance requirements for scalability/reliability
Very StrongDemonstrates deep understanding by asking insightful questions that reveal hidden complexitiesCandidate asks about seasonal variations in data volume, potential future expansion of the business use cases, and how batch/real-time ingestion needs might evolve

2. Technical design and architectural approach

This theme assesses your ability to design an architecture that meets both current and future needs.

What a strong response looks like:

  • You design a solid architecture that handles all the explicit and implicit use cases
  • You choose appropriate components for different parts of the architecture
  • You correctly identify specific ETL tools to handle each component of the architecture.
  • You consider advanced features like handling either/both batch/real-time ingestion patterns

Common pitfalls:

  • Creating a single ingestion pattern for all types of data
  • Failing to address key use cases
  • Overlooking important architecture attributes while creating data access patterns

Rubric:

LevelCriteriaExample
Very WeakProduces a highly inefficient architecture that fails to meet basic requirementsCandidate creates a single batch ingestion pattern for all data, missing the concept of real-time ingestion
WeakCreates a basic architecture with significant flaws in the components that need to be connected to each otherCandidate discusses ingestion patterns in-detail but glosses over transformation or loading patterns
StrongDesigns a strong architecture that accurately identifies each ETL component and aligns it with the use cases provided by the interviewerThe candidate designs an architecture that handles data ingestion, performs necessary transformations, and loads the data into a storage system
Very StrongDesigns an optimized architecture that effectively handles the current use cases and is flexible enough to accommodate future needsThe candidate designs an architecture that can handle various types of data and volumes, while being optimized for both OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) use cases

3. Performance and scalability considerations

Interviewers assess your understanding of how to optimize the architecture for performance and scalability.

What a strong response looks like:

  • You propose relevant optimization techniques aligned with the stated requirements
  • You suggest data partitioning strategies for the “loading” part of ETL
  • You consider both latency and throughput for overall system performance

Common pitfalls:

  • Showing no consideration for performance or scalability
  • Suggesting optimizations without considering their trade-offs
  • Overlooking the impact of high reads vs high writes

Rubric:

LevelCriteriaExample
Very WeakShows no consideration for performance or scalabilityCandidate doesn't mention latency/throughput, partitioning, or any other optimization techniques
WeakMentions basic performance concepts but doesn't apply them effectively to the specific scenarioThe candidate suggests partitioning, but either chooses the wrong column for partitioning or fails to mention other optimization techniques
StrongProposes relevant optimization techniques aligned with the stated requirementsThe candidate recommends partitioning the data using the correct column(s) and explains how to manage high-volume loads to meet the required latency limits
Very StrongProvides a comprehensive optimization strategy that balances query performance, storage efficiency, and future scalabilityCandidate proposes an architecture that handles data partitions to handle both high reads and/or writes, while also meeting throughput and latency requirements, and also mentions any other optimization technique specific to the use cases mentioned by the interviewer

4. Communication and articulation

This theme evaluates how clearly you explain your thought process and design decisions.

What a strong response looks like:

  • You clearly explain your design choices using correct terminology
  • You articulate trade-offs for different design decisions
  • You use analogies or visualizations to explain complex concepts when appropriate

Common pitfalls:

  • Using incorrect terminology
  • Struggling to explain the reasoning behind your decisions
  • Providing overly technical explanations without connecting to business requirements

Rubric:

LevelCriteriaExample
Very WeakUnable to explain design choices or uses incorrect terminologyCandidate can't explain why they chose a particular technology for a component of the overall architecture and confuses terms like “latency” and “throughput”
WeakProvides basic explanations but struggles to articulate trade-offs or reasoning behind decisionsCandidate can describe ⅓ components of the ETL architecture, but has significant issue explaining the remaining ⅔
StrongClearly explains design choices and articulates trade-offs with correct terminologyCandidate explains their choice of tools and discusses the pros and cons of their ingestion, transformation, and loading strategies
Very StrongProvides clear, concise explanations that demonstrate deep understanding, using analogies or visualizations when appropriateCandidate uses a whiteboard to sketch out their architecture, explaining each component and how it relates to the interviewer's requirements, and uses a real-world analogy to explain complex design trade-offs

5. Adaptability and iteration

Interviewers want to see your ability to adapt your design based on new information or feedback.

What a strong response looks like:

  • You readily incorporate feedback and make appropriate adjustments to your design
  • You proactively consider and propose multiple alternatives based on different potential scenarios
  • You demonstrate flexibility in your thinking and approach

Common pitfalls:

  • Becoming defensive when receiving feedback
  • Sticking rigidly to your initial design without considering alternatives
  • Making only superficial changes when more fundamental adjustments are needed

Rubric:

LevelCriteriaExample
Very WeakRefuses to modify design or becomes flustered when receiving feedbackWhen told that the business expects a 10x increase in data volume next year, the candidate insists their current design is fine without considering modifications
WeakMakes minor adjustments but struggles to incorporate significant changesCandidate adds a partition when told about performance issues but doesn't consider more fundamental changes during the “loading” part of the ETL process
StrongReadily incorporates feedback and makes appropriate adjustments to the designWhen informed about new reporting requirements, the candidate quickly adjusts their respective “transformation” and “loading” layers to account for the new use case(s)
Very StrongProactively considers and proposes multiple alternatives based on different potential scenariosBefore being given any feedback, the candidate presents multiple architecture options optimized for different scenarios (e.g., optimized for write-heavy vs. read-heavy workloads) and discusses the trade-offs of each

Conclusion

Understanding these evaluation criteria will help you prepare more effectively for data engineering questions in your interviews. Remember, interviewers are not just looking for textbook-perfect answers, but for candidates who can think critically, communicate clearly, and adapt to changing requirements. As you practice, focus on developing these skills alongside your technical knowledge.

Keep in mind that different companies may emphasize different aspects of this rubric, but mastering these areas will put you in a strong position for any data engineering interview. Good luck with your preparation!

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