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Soft questions in a data analyst interview

Interviewers are not expecting perfectly prepared or scripted answers. They want to see your ability to communicate and think on your feet. They’re looking not only for technical knowledge but also for cultural fit. Listen carefully to what is being asked and consider what they really want to know.

Here I present 10 common questions from data analyst interviews. It’s a guide to help juniors and those new to technical interviews.

Business and Career

What unique skills do you think can you add to our team?

Good

You may hear “skills” and think you need to recite specific knowledge, like of database JOINs and indexes. This is okay but doesn’t answer why you are unique.

Better

Uniqueness is what makes you more attractive than other candidates. While preparing, go back to the job description and look for what problem this new hire will be solving.

  • Are you going to be part of a client-focused team? Will you need to have strong stakeholder management skills?
  • Will you be working on specific projects, such as migration?
  • Have you worked in this industry before?

Highlight your skills that match the job description. Show that you’ve understood their problem.

How do you keep your technical skills up to date?

Good

Share your reading list, your favourite Twitter accounts and any tutorials or projects you’ve been working on.

Better

Expand on this by talking about interesting things that are happening in the industry right now:

Relate these current trends to the technology the company is using and projects they’re working on.

  • Has their move to the Cloud been in the news recently?
  • Will they have to take more precautions with GDPR legislation?
  • Ask if they are considering moving to a new framework?

Describe an example where you played an active role in solving a business problem through an innovative approach

Good

Describe the project:

  • What you worked on
  • The challenges you faced
  • Your resulting approach
Better

Focus on your specific contributions, rather than that of the team. Show how you were:

  • Diplomatic and attentive with stakeholders and team members
  • Wrote documentation and were able to back up your approach.

Talk about customer or manager feedback and how you’re interested in continually improving, whether that’s through your own innovation or by taking on suggestions from your team.

Understanding Requirements

Provide an example where a customer extended the work after the scoping had been completed?

Good

Give an example of when you successfully worked under pressure. The interviewer uses this question to gauge your stress management ability.

Better

Be specific and realistic about how you deal with customers. The interviewer wants to see your empathy and assertiveness. Demonstrate that you:

  • Anticipate change and push back on timelines
  • Understand the tradeoff between quality and timeliness

Data Cleansing and Analysis

Is more data always better? Do you prefer raw or enriched?

Good

Describing the trade-offs between quality and quantity is a good start to show your understanding in this area:

  • Enriched, cleansed data is easy to work with depending on your tools of choice
  • More data can produce more complete data models if outliers are accounted for
  • Cleansed data takes longer to get into a normalized and enriched state
  • Raw, granular data is expensive to store and move around.
Better

It really depends on the project whether you go with quality or quantity. However, the trade-offs can always be tackled with tools:

  • AWS Kinesis, and similar tools can send data in streams to avoid batch loading and the maintenance that comes with that.
  • Data Lakes can be built with S3, Glue and Athena to keep costs down.
  • Machine learning models can be deployed to do initial data cleansing and perform data quality monitoring when building new data sets.

What steps do you go through when processing and cleansing data in a typical project?

Good

Describe the different steps of a typical data analyst process:

  • Exploration
  • Preparation
  • Modelling
  • Validation
  • Visualization

Focus on why data cleansing is important:

  • To find any anomalies and outliers
  • To remove duplication and incorrect data
  • It makes the data set easier to work with
Better

Take this one step further by talking about the best practices for data cleansing:

  • Taking an iterative approach and cleansing in logical chunks
  • Developing a plan to identify where errors are occurring and to identify the root cause
  • Verifying data is correct before it is signed off and allowed to flow into a model
  • Script out as much as possible so the process can be repeated or rolled back when required

Which tools are you familiar with? What’s your preference?

Good

This question isn’t only about which tools you use. It’s an opportunity to talk about your experience with each of those tools. Analysts should be familiar with Excel, SQL, a visualisation tool and a statistical analysis tool or scripting language.

Show the interviewer that you are familiar with a suite of tools, even better if they are the preferred tools for the role you are interviewing for. Be sure to use examples that show your level of experience and the kind of tasks you use them for.

  • Excel – projects to aggregate data using Pivot Tables, and visualize the results using conditional formatting and graphs.
  • SQL – projects to JOIN multiple datasets together and schedule them to run with a stored procedure.
  • Visualization tool – projects to track the progress of sales over time using multiple graphs with thought put into the colour, graph type and what the end user is trying to get out of it.
Better

Take this one step further. Talk about any new, or popular, tools you have read about or tried using. Show an interest in big data tools like Hadoop and Spark, scripting languages like Python, and libraries like D3.

Describe an example of a complex analysis you’re proud of

Good

The interviewer is looking for examples where you can enthusiastically describe what you did and what the result was. This is hopefully the work you enjoy and may enjoy in the new role.

Make sure your example is:

  • Relevant to the role
  • Something you are genuinely proud to have worked on
  • Not sheer luck or where you contributed only a small part of a team project
  • True! Don’t embellish a story.
Better

Talk about how this project helped push you forward:

  • Could you change a process that saved time or money?
  • Did your analysis feed into future work?
  • Did this success help you discover what you enjoy and what you are good at?

Data Visualization

What tools have you used to publish data to end users?

Good

This is an opportunity to show your understanding of the range of options for data visualization and when they are appropriate.

  • Excel – if you have worked in a startup, small organization or prefer Excel as a one-stop-shop. There is nothing wrong with using Excel for small datasets that don’t contain sensitive information.
  • Enterprise Tools – if you have worked in a bigger organization you may have used Tableau, PowerBI or MicroStrategy. The associated licensing and training costs make these more expensive, but provide a secure way to connect from database to visualization layer.
  • Statistical Tools – if you have worked in academia or scientific fields you may have used SAS, R, Jupyter notebooks or SPSS to present data. These are much more specialized tools but are relevant for roles in these fields.
  • Web-based Tools – frameworks and libraries like D3 and HighCharts are increasingly common for infographics and web-based data visualizations.
Better

Show that you know when to use one over the other and the drawbacks given each scenario:

  • Excel
    • Great for quick analysis that is accessible and user-friendly
    • Isn’t a secure way to share sensitive data and multiple copies may end up on individuals machines
  • Enterprise Tools
    • Provide a secure, scalable way to connect the database to the visualization tool
    • Expensive licensing arrangements and can be time consuming to set up and train users
  • Statistical Tools
    • Specialized tools that allow the code for aggregation and visualization to be run in the same place.
    • Conflicting libraries and package versions make it hard to share, less technical or skilled users might find it difficult to get started
  • Web-based Tools
    • Generally beautiful to look at and interactive
    • Requires a different set of skills to set up and maintain, not always an appropriate way to publish sensitive data

What form of user support would you include?

Good

The interviewer is looking for your support preferences. Once you have completed a project, what do you prefer:

Better

Show you can evaluate what is best for the user and identify what ongoing help they might need.

  • Would you present your findings differently if dealing with senior managers?
  • Would you consider the role of your end user? A fellow analyst may have different questions than a colleague in sales or marketing.
  • What would you do if your audience looked bored in your presentation?

Conclusion

As hard as it is to believe, interviewers don’t want perfectly prepared answers. They know you’re human and are looking for not only technical knowledge but cultural fit. How you communicate and think on your feet is a vital part of their assessment.

Good luck with your preparation!

Helen Anderson

Data enthusiast and builder of AWS things. http://www.helenanderson.co.nz/