As companies depend more and more on data analytics to make crucial strategic decisions, the importance of the data analyst has only increased. Depending on their role, seniority, and the company’s needs, a “typical” data analyst might end up doing anything from writing algorithms to explaining the importance of a particular finding to the C-suite. In addition, many of these roles are highly specialized; a data analyst who works in healthcare, for example, will need to rely on a totally different knowledge base than a colleague who works in finance.
Given the importance of the role, data analyst salaries are predictably high, especially in conjunction with years of experience and certain skills/specializations. But in order to land one of these positions, you’ll need to figure out the data-analyst landscape, including skills and education requirements.
Typical Data Analyst Job Posting
A “typical” data analyst job posting will emphasize the ideal candidate’s mix of technical skills, analytical abilities, and soft skills (such as communication). Main areas of responsibility 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.
Depending on the position itself, the exact technical qualifications will vary (“strong knowledge of relevant tools and platforms” and “strong analytical skills” are two bullet-points that pop up again and again in postings, but those are broad; specific companies usually want professionals who can work with their specific tech stacks). On the “soft skills” front, however, all companies will generally expect a data analyst to do the following:
- Display a strong initiative (i.e., self-management).
- Act as a team player with both technical and non-technical colleagues
- Excellent communication skills with knowledge of business and technical terms.
Being well-rounded is key; you need to display technical andcommunications skills in equally strong ways to land the job. Which brings us to…
Data analysts must understand how to use various types of data-analytics software, including:
- Postgresql (an open-source relational database management system)
- RapidMiner (a data-science platform used by many companies)
- Knime (an analytics platform)
- Datawrapper (an online tool for creating charts and visualizations)
- Tableau (another visualization tool)
- SAS Sentiment Analysis
- Google Fusion Tables
- Apache Hadoop
That’s in addition to knowing R and Python, two programming languages that are driving data analytics at the moment (although R is definitely more of a language for academics and research projects; within many organizations, Python’s ubiquity and scalability is making it the language of choice for commercial endeavors). Knowledge of SQL, which is used to manage data within relational database management systems (RDBMS) is also essential for work at many companies.
There are also some certifications that data analysts can obtain, including:
- CCA Data Analyst
- SAS Certified Data Scientist
- Data Science Council of America Certification
- Microsoft MCSE: Data Management and Analytics
These certifications not only look good on your application materials, but they can sometimes translate into significant salary bumps (depending on the company).
Typical Data Analyst Interview
When interviewing a data analyst for a position, a hiring manager generally wants to determine a few things. Does the analyst have the skills necessary for the position? Are they capable of gleaning actionable insights from whatever datasets they analyze? And can they effectively communicate crucial trends and important findings to other stakeholders within the business?
Any data analyst interviewing for a position must be prepared to address:
- How well they communicate with stakeholders.
- Their skill with various types of data analytics software.
- Their approach to data-analytics projects.
- How they handle pressure (complete with examples).
- What they like about data analytics.
As with all types of job interviews, providing relevant examples from your experience is key. In addition, the prospective employer may give the data-analyst candidate a few problem sets to solve. These tests can either be take-home or in-office, and often address a practical issue (for example, the candidate might be asked to demonstrate the best practices for cleaning a dataset).
What to Include on a Data Analyst Résumé
In your data analyst résumé (and this also works for a business data analyst or a data scientist résumé, as well), your experience section should include bullet-points that focus on your technical, analytical, and “soft skills,” all while showing your material impact on your former companies.
For example, a bullet-point along the lines of “Led team to deliver key insights that boosted revenue by 25 percent” will tick off all those boxes: communication and leadership, analytical abilities, and real results.
As with all résumés and CVs, it’s also incredibly important to list all relevant skills and certifications—but take care not to throw on anything outdated or unrelated to the position. If it’s been a few years since you last took a stab at your résumé, make sure you update it.