Essential Work Sample Exercises for Hiring Top Analytics Engineers

Analytics Engineers play a crucial role in modern data teams, bridging the gap between data engineering and data analysis. They transform raw data into analytics-ready datasets, build and maintain data models, and ensure that business users can access reliable, well-documented data for decision-making. Finding the right Analytics Engineer requires evaluating both technical skills and problem-solving abilities in real-world scenarios.

Traditional interviews often fail to reveal how candidates approach actual work challenges. Technical interviews might assess knowledge of SQL or specific tools, but they rarely demonstrate how candidates think through complex data problems or communicate their solutions. This is where carefully designed work samples become invaluable.

Work samples provide a window into how candidates approach real tasks they'll face on the job. For Analytics Engineers, this means seeing how they model data, optimize queries, document their work, and collaborate with stakeholders. These exercises reveal not just technical proficiency but also attention to detail, communication skills, and problem-solving approaches.

The following work samples are designed to evaluate the essential skills of an Analytics Engineer in action. They simulate common challenges these professionals face and provide a structured way to compare candidates objectively. By implementing these exercises in your hiring process, you'll gain deeper insights into each candidate's capabilities and fit for your specific data environment.

Activity #1: Data Modeling Exercise

This exercise evaluates a candidate's ability to design efficient, intuitive data models that transform raw data into analytics-ready structures. Data modeling is a fundamental skill for Analytics Engineers who must balance technical considerations with business usability.

Directions for the Company:

• Provide the candidate with sample raw data files (CSV or JSON) representing 2-3 related business entities (e.g., orders, customers, products).
• Include a brief description of the business context and 3-5 key questions business users need to answer with this data.
• Ask candidates to design a dimensional model (fact and dimension tables) that would support these analytical needs.
• Allow candidates to use their preferred diagramming tool (e.g., Lucidchart, draw.io) or even pen and paper if the interview is in-person.
• Allocate 45-60 minutes for this exercise.
• Provide access to documentation on your company's data modeling conventions if applicable.

Directions for the Candidate:

• Review the provided raw data files and business requirements.
• Design a dimensional model (fact and dimension tables) that efficiently supports the analytical needs.
• Document your model with a diagram showing tables, fields, and relationships.
• Include brief explanations of key design decisions, particularly any trade-offs you considered.
• Be prepared to explain how your model supports the business questions and how it could be extended for future needs.
• Submit your diagram and documentation before the deadline.

Feedback Mechanism:

• After reviewing the submission, provide feedback on one aspect the candidate handled well (e.g., efficient design, good documentation) and one area for improvement (e.g., missing relationships, overlooking a business requirement).
• Ask the candidate to explain how they would modify their design based on the improvement feedback, giving them 10-15 minutes to sketch or explain the changes.
• Observe how receptive they are to feedback and how they incorporate it into their revised approach.

Activity #2: SQL Query Optimization

This exercise assesses a candidate's ability to write efficient SQL queries and optimize existing ones—a critical skill for Analytics Engineers who must ensure that data models perform well under various query patterns.

Directions for the Company:

• Prepare a sample database with realistic data volume (can be a subset of your actual data with sensitive information removed).
• Create 2-3 poorly performing SQL queries that answer legitimate business questions but have clear optimization opportunities.
• Document the current query execution time or explain plan.
• Provide database schema documentation and access to a development environment where candidates can run and test queries.
• Allocate 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the provided SQL queries and database schema.
• Identify performance issues in the queries.
• Rewrite the queries to improve performance while maintaining the same output.
• Document your optimization approach, explaining the specific techniques you used and why.
• Include before/after execution plans or timing metrics if available.
• Be prepared to discuss additional optimizations you would consider if you had more time or control over the database structure.

Feedback Mechanism:

• Provide feedback on one optimization technique the candidate implemented effectively and one additional opportunity they missed.
• Ask the candidate to incorporate the missed optimization into one of their queries, giving them 10-15 minutes to revise.
• Discuss the impact of their revisions on query performance and any trade-offs involved.
• Observe how they balance technical optimization with readability and maintainability.

Activity #3: Data Transformation Pipeline Design

This exercise evaluates a candidate's ability to design and implement data transformation logic, a core responsibility of Analytics Engineers who must convert raw data into business-ready formats.

Directions for the Company:

• Provide sample source data files with realistic business data (e.g., sales transactions, user events).
• Include a description of the desired output format and business rules for transformations.
• Specify any data quality issues that need to be addressed (e.g., missing values, duplicates).
• Allow candidates to use SQL, Python, or other tools relevant to your stack.
• Provide access to documentation for any specific tools or frameworks you use.
• Allocate 60-90 minutes for this exercise.

Directions for the Candidate:

• Review the source data and requirements for the transformed output.
• Design and implement a data transformation pipeline that converts the source data to the required format.
• Include data quality checks and error handling in your solution.
• Document your transformation logic, including any assumptions you made.
• If time permits, include tests that validate your transformation results.
• Be prepared to explain how your solution could be scheduled and monitored in a production environment.

Feedback Mechanism:

• Provide feedback on one aspect of the pipeline design that demonstrates good practices and one area where the approach could be more robust or maintainable.
• Ask the candidate to enhance their solution based on the improvement feedback, giving them 15-20 minutes to implement changes.
• Discuss how their revised approach addresses the feedback and any additional considerations they took into account.
• Evaluate their ability to balance technical correctness with practical implementation concerns.

Activity #4: Business Requirements Translation

This exercise assesses a candidate's ability to translate business requirements into technical specifications—a crucial skill for Analytics Engineers who must bridge the gap between business needs and technical implementation.

Directions for the Company:

• Prepare a realistic business scenario with a stakeholder request for a new dashboard or data product.
• Include ambiguous or incomplete requirements that require clarification.
• Provide context about existing data sources that might be relevant.
• Consider having a team member play the role of the business stakeholder for a mock requirements gathering session.
• Allocate 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the business request and available data sources.
• Identify questions you would ask to clarify requirements.
• If a mock stakeholder is available, conduct a brief requirements gathering conversation.
• Create a technical specification document that outlines:
• Data sources needed
• Transformations required
• Metrics definitions and calculations
• Potential challenges or limitations
• Implementation approach and timeline
• Be prepared to present your specification and explain how it addresses the business needs.

Feedback Mechanism:

• Provide feedback on one aspect of the requirements translation that effectively bridged business and technical considerations and one area where more clarity or detail would be beneficial.
• Ask the candidate to revise the portion of their specification that needs improvement, giving them 15 minutes to enhance it.
• Discuss how their revised approach better addresses the business needs or provides more implementation clarity.
• Evaluate their ability to communicate technical concepts to business audiences and vice versa.

Frequently Asked Questions

How long should we allocate for these work samples in our interview process?

Each exercise is designed to take 45-90 minutes. We recommend selecting 1-2 exercises most relevant to your specific needs rather than attempting all four. The entire work sample portion of your interview process should typically not exceed 2-3 hours to respect candidates' time.

Should these exercises be conducted live or as take-home assignments?

Both approaches have merit. Take-home assignments allow candidates to work in their preferred environment without time pressure, while live exercises reveal real-time problem-solving abilities. Consider your priorities and the seniority of the role. For senior positions, a hybrid approach works well: a take-home component followed by a live discussion of their solution.

How should we evaluate candidates who use different approaches than we expected?

Focus on the effectiveness of their solution rather than strict adherence to a specific approach. Analytics Engineers often bring diverse experiences and may introduce valuable new perspectives. Evaluate whether their approach solves the core problem efficiently and maintainably, even if it differs from your current practices.

What if candidates don't have experience with our specific tech stack?

These exercises are designed to evaluate fundamental skills that transfer across technologies. Allow candidates to use tools they're comfortable with, then assess their ability to apply those skills to your environment. Strong Analytics Engineers can adapt to new tools quickly if they understand core data principles.

How do we ensure these exercises don't disadvantage candidates from underrepresented groups?

Design exercises that minimize reliance on specific domain knowledge that might be more common in certain demographics. Provide clear context and documentation. Consider offering flexibility in scheduling for candidates with caregiving responsibilities. Evaluate solutions based on documented criteria rather than subjective impressions to reduce unconscious bias.

Should we compensate candidates for completing these exercises?

For extensive take-home assignments (over 2 hours), consider offering compensation, especially for senior roles. This demonstrates respect for candidates' time and expertise while potentially increasing completion rates and effort quality.

The right Analytics Engineer can transform your organization's ability to leverage data for decision-making. By incorporating these practical work samples into your hiring process, you'll gain deeper insights into candidates' real-world capabilities and identify those who can truly drive value through data.

For more resources to improve your hiring process, check out Yardstick's AI Job Descriptions, AI Interview Question Generator, and AI Interview Guide Generator. You can also find a detailed job description for Analytics Engineers at https://yardstick.team/job-description/analytics-engineer.

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