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How to hire a Data Analyst

Data analysts answer business questions using SQL, dashboards, and clear communication. They connect raw data to real decisions. Every department wants one, and the good ones get hired fast.

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Data Analyst

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What is a Data Analyst?

A Data Analyst turns data into decisions. They start with a business question, which is often vaguer than it first looks. They work out which data can answer it. They query and clean that data and check the result against reality. Then they present a finding someone can act on. Analysts also build and maintain reports so a team can see what is happening without asking. The hard part is rarely the query. It is framing the question and knowing when the answer cannot be trusted.

Also called
Business Intelligence Analyst, Reporting Analyst, Insights Analyst
Function
Analytics and business intelligence
Level
Entry to senior individual contributor
Reports to
Analytics Manager, Head of Data, or a business function head
Common stack
SQL; Excel or Sheets; Tableau, Power BI or Looker; Python or R
Next step
Senior Analyst, Analytics Lead, Data Scientist, or Product or Finance roles

The hiring challenge

Good data analysts are rare because the role needs both SQL depth AND storytelling. Most candidates are strong in one and weak in the other. Some write perfect SQL but cannot explain what the numbers mean to a VP. That is only half an analyst. Others tell great stories but cannot join three tables. That is the other half. You need both in one person.

What to look for in a Data Analyst

Three things matter. SQL fluency: can they write a fairly complex query on their own? Business judgment: when revenue drops 15%, do they check the right things in the right order? Clear communication: can they turn a SQL result into a sentence a non-technical person can act on?

Also look for comfort with the number and date formats your business uses. Look for experience with the BI tools your team uses, such as Metabase, Looker, Tableau, or Power BI. If your analytics team supports global teams, check that they can work across time zones.

How to structure the interview

A strong data analyst interview has three parts. First, a SQL challenge. Give them a schema and a business question that needs joins, aggregation, and a window function. Check that the query is correct and easy to read. Note whether they ask questions about the schema. Second, a case study. Give them a realistic business problem, such as falling revenue, rising churn, or a weak feature launch. Ask them to walk through how they would dig in. Score whether they plan their approach before diving in. Third, a dashboard review. Ask them to walk through something they built and the decisions it led to.

Common mistakes when hiring Data Analysts

Testing SQL trivia instead of SQL thinking. Asking "what is the difference between WHERE and HAVING" tests memory. Asking "given this schema, find users who churned after their first month" tests thinking. Test thinking.

Ignoring the communication round. An analyst who cannot present findings clearly will build dashboards nobody opens. Always include a round where the candidate explains something to a non-technical audience.

Not checking for business context. A good data analyst asks "why does this matter?" before running a query. They do more than write SQL on request. If a candidate jumps straight to code without understanding the business question, that is a red flag.

Key skills for a Data Analyst

  • SQL (joins, aggregation, window functions)
  • Dashboard design and storytelling
  • Business judgment
  • Data investigation methodology
  • Communication to non-technical stakeholders
  • A/B test interpretation
  • Excel and spreadsheet fluency
  • BI tool proficiency

What a great interview looks like

Coding

"Write a SQL query to compute weekly retention cohorts from a signups table and a logins table."

Scenario

"Revenue dropped 15% last week. What are the five things you would check first, and why?"

Video

"Show me a dashboard you built. What decisions did it help your team make?"

Voice

"Tell me about a time your analysis changed a decision. What was the question, what did you find, and what happened?"

Scenario

"Your manager asks you to prove that a new feature increased engagement. How do you set up the analysis?"

You can use these questions in Goodfit as they are, or edit the questions and the scoring for your team.

Recommended interview process

  1. 1

    Round 1: AI Voice Interview15 to 20 min

    Questions about past analysis work, business judgment scenarios, and how they explain results.

  2. 2

    Round 2: SQL + Case Study45 min

    A SQL challenge on a real-looking schema, plus a walkthrough of how they would investigate a business problem.

  3. 3

    Round 3: Hiring Manager Interview30 min

    Dashboard review, team fit, and a talk about the tools your team uses.

In Goodfit, the AI interview runs the first round for every applicant. Skill tests, coding tests and roleplays can run in Goodfit too. Your team reads the scorecards and joins for the later conversations.

FAQ

Data Analyst hiring questions

What does a data analyst do?
They answer questions with data. First they find out what the person asking needs to decide. They find the right source and write SQL to pull and sum it. They clean it and fix things that do not match. They check that the result makes sense. Then they present a clear conclusion that goes beyond a table. They also build dashboards and keep metrics defined so every team uses the same numbers. When a metric moves in an odd way, they find out why.
What is the difference between a data analyst and a data scientist?
An analyst mostly explains what happened and why. They use queries, summaries, and charts. A data scientist mostly predicts what will happen or measures cause and effect. They use statistical models and machine learning. The line is blurry, and inflated titles are common. So read the duties. If the role is dashboards, metric definitions, and business questions, it is analysis, whatever the job post calls it.
What is the difference between a data analyst and a business analyst?
A data analyst works mostly with data to produce findings. A business analyst works mostly with people and processes. They gather requirements, write down workflows, and spell out what software should do. Some business analyst roles involve a lot of reporting, so there is overlap. But the core skills differ. One side needs SQL and statistics. The other needs skill at drawing out what people need and designing processes.
What skills does a data analyst need?
SQL is the must-have, and most screening starts there. They need spreadsheet skills, since spreadsheets are still what most people trust. They need a visualization tool. More important, they need the judgment to pick a chart that does not mislead. They need basic statistics, like sampling and variance. That helps them avoid reading random noise as a trend. They need business context so they explain a number as well as report it. And they need to communicate, because an analysis nobody acts on achieves nothing.
Do data analysts need Python?
Not always, but it widens what they can do. SQL and a visualization tool cover much of an analyst's work. Python helps with cleaning steps you repeat often. It helps with data that is too awkward for SQL. It helps with statistics beyond averages. It can also automate a report someone rebuilds by hand every month. Learning Python is also the most common path from analysis to data science.
How is a data analyst's work measured?
Less by how much they produce, and more by whether decisions changed. Fair measures include how fast they turn around requests. Another is how widely used and accurate their dashboards and metrics are. Another is fewer one-off requests because self-serve reporting got better. And another is the decisions their analysis shaped. Counting dashboards is a poor measure. The best analysts often cut the number of dashboards while answering more questions.
What qualifications do you need to become a data analyst?
Many analysts have a degree in economics, statistics, engineering, or business. It is not required. Analytics is one of the easier technical careers for career changers to enter. Most employers screen on proven SQL skill. They also check whether a candidate can explain what a result means, as well as produce it. A portfolio of real analysis that shows the reasoning usually beats a certificate.
What interview questions should I ask a data analyst?
Give a vague request on purpose. For example: why did revenue drop last month? Score the questions they ask before they touch any data. Show a chart with a misleading axis or a survivorship problem, and ask what is wrong with it. Give a SQL task with a join and a deduplication. Ask about a time they reached the wrong conclusion and how they found out. Ask how they would explain a result that is not statistically significant to an executive who wants an answer.
How do you screen data analysts at volume?
A short SQL test is the fastest first filter, and it tracks well with skill. On its own, though, it only checks how well people write queries. Pair it with a structured exercise on reading results. That could be a chart to critique or a vague request to scope. Score it on a fixed rubric. In analytics, the costly mistake is rarely a wrong query. It is a confident answer to the wrong question.

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