Screening checklist
Data Analyst Screening Checklist
Evaluate data analyst candidates with this structured screening checklist covering SQL skills, analytical thinking, visualization tools, and scoring.
The short answer
Screening Data Analyst candidates well means fixing the pass criteria before the first résumé is opened. The checklist below covers résumé triage, a short phone screen with model answers, a technical check with passing criteria, and the deal-breakers that end a conversation early.
- Must have: Advanced SQL skills including window functions, CTEs, and query optimization
- Must have: Ability to clean, transform, and validate large datasets from multiple sources
- Must have: Experience building dashboards and reports for non-technical stakeholders
- Deal-breaker: Cannot write basic SQL queries or explain fundamental statistical concepts
Cohesyve scores every applicant against a role-specific assessment. Ten candidates free, no card.
Data analysts transform raw data into actionable insights that drive business decisions. Screening for this role demands assessing both technical skills like SQL and visualization tools and softer capabilities like storytelling with data. This checklist ensures your evaluation is comprehensive, consistent, and aligned with the analytical needs of your organization.
Résumé screening
- Degree in Statistics, Mathematics, Economics, Computer Science, or a related quantitative field
- Minimum one year of experience in a data analysis or business intelligence role
- Proficiency in SQL demonstrated through work experience or project descriptions
- Experience with data visualization tools such as Tableau, Power BI, or Looker
- Familiarity with Python or R for data manipulation and statistical analysis
- Track record of delivering data-driven recommendations that influenced business outcomes
Must-have qualifications
- Advanced SQL skills including window functions, CTEs, and query optimization
- Ability to clean, transform, and validate large datasets from multiple sources
- Experience building dashboards and reports for non-technical stakeholders
- Strong understanding of descriptive and inferential statistics
- Excellent written and verbal communication skills for presenting findings
Phone screen questions
Ask every candidate the same set, in the same order, and note the answer against the model before you form a view.
Tell me about a time your analysis directly influenced a business decision.
A strong answer The candidate should describe a specific scenario, explain the data sources used, the methodology applied, and the recommendation made. They should quantify the impact on revenue, efficiency, or customer satisfaction and describe how they communicated findings to stakeholders.
How do you ensure the accuracy and reliability of your data before starting an analysis?
A strong answer A strong answer includes data validation checks, handling missing values, cross-referencing with source systems, and documenting assumptions. The candidate should show a disciplined approach to data quality rather than jumping straight to analysis.
Which data visualization tool are you most comfortable with and why?
A strong answer The candidate should name a specific tool and explain its strengths for their use cases. They should discuss how they design dashboards for clarity, choose the right chart types, and tailor visualizations to the audience.
Describe a situation where you had to work with incomplete or messy data. How did you handle it?
A strong answer The candidate should explain their approach to identifying data gaps, imputing or excluding missing records, and communicating limitations to stakeholders. They should show pragmatism and transparency about data quality trade-offs.
How do you prioritize multiple data requests from different teams?
A strong answer The candidate should describe a prioritization framework considering business impact, deadlines, and effort required. They should mention proactive communication with requestors, setting expectations, and batching similar analyses when possible.
Cohesyve
Let the screen run itself
Cohesyve puts a Data Analyst assessment between the application and the phone screen, so the calls you make are with people who have already cleared the bar above.
Technical screening
| Area | What to test | Passing criteria |
|---|---|---|
| SQL Proficiency | Provide a database schema and ask the candidate to write queries involving joins, aggregations, window functions, and subqueries to answer business questions. | Queries are syntactically correct, efficient, and produce the expected results. The candidate explains their query logic and considers performance implications. |
| Data Visualization | Give the candidate a sample dataset and ask them to build a dashboard or set of visualizations that tell a compelling data story. | Visualizations are clear, appropriately labeled, and use the right chart types. The candidate articulates insights and design choices that make the data accessible to a non-technical audience. |
| Statistical Analysis | Present a business scenario requiring hypothesis testing, correlation analysis, or trend identification. Ask the candidate to outline and execute the appropriate statistical approach. | The candidate selects the correct statistical method, interprets results accurately, and explains findings in business terms with appropriate caveats about statistical significance. |
| Data Cleaning and Transformation | Provide a messy dataset with missing values, duplicates, and inconsistencies. Ask the candidate to clean and prepare it for analysis using Python, R, or spreadsheet tools. | The candidate systematically identifies and addresses data quality issues, documents their cleaning steps, and produces a reliable dataset ready for analysis. |
Fit and deal-breakers
Good signs
- Demonstrates curiosity and asks probing questions about the data and the business context
- Shows patience and thoroughness when dealing with ambiguous or incomplete data
- Communicates complex findings in simple, accessible language
- Collaborates well with cross-functional teams including marketing, product, and engineering
- Takes ownership of their analyses and proactively suggests areas for deeper investigation
Deal-breakers
- Cannot write basic SQL queries or explain fundamental statistical concepts
- Presents analysis without acknowledging data limitations or potential biases
- Shows no interest in understanding the business context behind the data
- Fails to communicate findings clearly or relies solely on technical jargon
Scoring rubric
Technical Proficiency
30%- Writes efficient and accurate SQL queries for complex data retrieval tasks
- Demonstrates strong skills in data visualization and dashboard design
- Applies appropriate statistical methods and interprets results correctly
Analytical Thinking
30%- Breaks down complex business questions into structured analytical approaches
- Identifies patterns, trends, and anomalies in data independently
- Provides actionable recommendations supported by data evidence
Communication Skills
20%- Presents data findings clearly to both technical and non-technical audiences
- Creates well-organized reports and dashboards with appropriate context
- Asks thoughtful questions to clarify ambiguous requirements
Cultural Fit and Collaboration
20%- Works effectively with cross-functional teams to understand business needs
- Shows initiative in identifying new opportunities for data-driven improvement
- Demonstrates reliability and accountability in delivering analyses on time
Common questions
What is the most important skill to screen for in a data analyst?
SQL proficiency is the most critical technical skill since analysts spend the majority of their time querying databases. However, equally important is the ability to translate data findings into actionable business insights, which requires strong communication and analytical thinking.
Should we require a specific degree for data analyst candidates?
While degrees in quantitative fields are common, they are not strictly necessary. Many strong data analysts come from non-traditional backgrounds and have built their skills through bootcamps, certifications, or self-study. Focus on demonstrated ability rather than credentials alone.
How do we assess a data analyst candidate who has only academic experience?
Evaluate their academic projects, personal portfolio, or Kaggle contributions as proxies for professional experience. Focus the technical screening on practical exercises rather than behavioral questions about workplace scenarios they have not yet encountered.
Cohesyve · Skill assessments for hiring
Assess Data Analyst candidates before you interview them
Cohesyve turns a job description into a role-specific assessment with a scoring rubric. Each candidate gets a different version, so questions cannot be shared between applicants.
1,500+
assessments completed
50%
faster time-to-hire
90%
completion rate
5 min
from JD to assessment
No credit card · 10 free candidates · Plans sized to your hiring volume
From the blog