Data analyst interview questions focus not only on your analytical skills (always useful in a data analyst job) but also your “soft skills” such as communication and empathy. Keep that in mind if you’re applying for data analyst jobs.
Over the past several years, data analysts have become only more vital to companies’ long-term strategies. That means the typical data analyst job features a variety of tasks; depending on the firm and its mission, an analyst could find themselves writing algorithms in the morning and communicating with the C-suite in the afternoon. Analyzing data, and then translating the results into plain language that’s digestible by executives and other teams, is ultimately critical.
For example, a “typical” data analyst job posting might include:
- Work with end users to determine data/reporting requirements.
- Collaborate with managers to formulate requirements for data.
- Develop appropriate documentation.
- Support data inquiries and questions from the broader organization.
- Define database quality and address any data quality issues.
- Establish strong communication with other teams.
As you can tell from those bullet-points, attention to detail is important. “Read the job description thoroughly,” suggested Jen Hood, career coach at The Career Force and the Owner of Avant Analytics. “This sounds like basic advice, but you’d be surprised at the number of people who clearly haven’t taken time to do this.”
Granular attention to all job-posting details will inevitably pay off during the data analyst interview questions. First and foremost, a data analyst must understand the finest nuances of the task at hand; if the job’s requirements include cleaning up the database, for instance, come prepared with stories of how you did just that in your previous roles (or learned about related techniques in school, if this is your first job as a data analyst intern).
Make sure to also come prepared with an optimized data analyst resume that highlights what skills or experiences that would make your perfect for this specific role (make sure to match skills up to highlight those skills).
What are the challenges faced in the data analyst position?
Data preparation for use by teams throughout the organization is vital; your answers to many data analyst interview questions will ultimately address that very thing. (It also pays to keep in mind that the job requirements for a data analyst may vary considerably from those for a data scientist or data engineer; contrary to what many may believe, those job titles are not interchangeable.)
“Datasets provided in college and most educational programs are clean and easy to use,” said Joshua Jones, CEO of StrategyWise, a data science consulting firm. “In the real world, data is almost never ready to use, and oftentimes not even usable at all. A lot of effort goes into the unglamorous process of cleaning data and preparing it for use.”
Jones added: “Translating technical and statistical insights into business-ready soundbites is often a challenge for the technically inclined, but it is also a hallmark of the most successful. The true ‘unicorns’ in data science are the ones who can not only build models, but communicate their results effectively to the C-suite executives.”
This is one of the more challenging aspects of being a data analyst, and describing how you’re prepared for it is one of the key challenges during data analyst interview questions. In short, you have to describe, on a very tactical level, how you translate data into digestible content for others. What stories can you tell about how you made a dataset understandable to those who aren’t that knowledgeable about data?
A great data analyst understands their audience. Not all ‘translations’ of data will work, and the numbers may have to be distilled into several unique forms, particularly visualizations. Your ease with various tools and outputs is key to acing the data analyst interview.
What questions are typically asked during a data analyst interview?
“We often use case studies, and frequently will offer a redacted version of a project we’ve just completed,” Jones said. “Things like: ‘How would you approach a project where the client would need to predict X, given Y?’”
It’s salient advice. During the data analyst interview questions, expect the company to provide anonymized queries relevant to their own work or future projects. It’s a virtual certainty that interviewers are looking to hire someone who can “solve for x” on an issue the company is currently facing.
“Aside from the general interview questions (‘Tell me about yourself,’ etc.), I focus on skill application questions,” Hood added. “Degrees, training, and certification are all great to have. I want to know how you put all those skills you learned into practice.”
Key certifications that many data analysts obtain (and which may come up in an interview) include:
- CCA Data Analyst
- SAS Certified Data Scientist
- Data Science Council of America Certification
- Microsoft MCSE: Data Management and Analytics
From the interviewer’s perspective, a question along the lines of, “What Tableau related project are you most proud of?” yields a much more insightful answer than “Do you know how to use Tableau?” For candidates, that means your preparations should focus on how to describe your past experiences in ways that will show you can meet the prospective employer’s goals. (If you’re actually asked about your Tableau knowledge, in other words, give an answer that demonstrates how you’ve used it.)
Hood also offers up these sample questions data analysts should be prepared for:
- Tell me how you work with others in the business to gather inputs for your analysis.
- How do you usually communicate the results of your analysis?
- What skills are you currently working on?
Candidates should also prepare for questions directly related to the job description. “If [the role] requires Python knowledge and is for retail products, an interviewer may want to know how you have used Python to analyze sales trends,” Hood noted.
What questions should candidates ask in an interview for this position?
Asking direct questions is always useful. “Am I going to be working on projects that have never been tried before, or expanding existing work?” Jones said. “Digging deeper into this will help the candidate understand the risk/reward of their position.
“True research and development projects have a high failure rate, and candidates need to be comfortable with a lot of trial and error,” he continued. “Expansion of existing works are often lower risk and better for those who want predictable outcomes.”
It’s a great tip. Companies may mean well when hiring a data analyst, but your tolerance for failure—and potential loss of work—may be low. A role tackling new initiatives carries expectations. If this is the case, try to gauge the tolerance level of the company. If they’re looking for a quick turnaround on a massive problem, the internal politics may be more trouble than the job is worth.
This article was originally published on Dice.
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