I have been applying to so many roles and have not heard back! I have no idea what is wrong with my data analyst resume!
Does that sound like you? There’s a lot of advice out there on what makes a good resume; whether to make it pretty or not. However, no one has actual data on what really works.
JobStep is the first company that has actual data on what really works. In 2021, we applied to over 26,000 jobs on behalf of jobseekers. We know what needs to be in a resume in order to get interviews.
How to craft an ideal data analyst resume
It sounds obvious but your resume needs to prove that you can do the work of a data analyst. Let’s recap what a data analyst does and share more information about the role, responsibilities, and what employers look for in a candidate.
A data analyst leverages company data to build comprehensive reports and presentations that answer difficult questions. They use tools like Excel, SQL, Tableau, PowerBI, Python, or R. Understanding these tools are typically core requirements for any data analyst role paying above $65K.
Your day-to-day job responsibilities as a data analyst will include:
- Tracking down relevant data from various data sets and sources.
- Finding meaningful insights within that data to drive business decisions.
- Presenting the data to tell a story to relevant departments within your organization.
Your resume will need to show a potential employer that you have experience analyzing data and using it to come to a conclusion. We will speak more to this throughout this guide. We also will include sample data analyst resumes at the end that you can use to model your resume after.
What are recruiters looking for in a data analyst resume?
Here the questions recruiters ask themselves when they review your experiences on your resume. We recommend you take some time to write out these answers before you even write or update your resume.
- What was the goal of the project? What was the business problem you were trying to solve?
- What was the data that you worked with?
- Describe the data you were using. What features or variables were you looking at?
- How did you collect the data?
- What approach did you use to analyze the data or research the key variables?
- Did you find an answer? How did you prove you were right? What metric did you evaluate?
- What kind of impact did you have on the company? Which metric did you use to measure business success? What did you learn from the project?
- How did you present your data?
After you answer the above questions, you can create resume bullet points that showcase your experience and skillsets.
What education should I have on a data analyst resume?
Without the right education on your data analyst resume, you’ll almost certainly wind up in the rejection pile. Thankfully, the type of education you put on your resume doesn’t matter as much as showing how you’ve used the skills you’ve learned.
Broadly speaking, there are three ways that one could build the education required for a data analyst career. Traditional education, online courses, or learning on the job. Most data analysts combine 2 out of 3 as they progress through their careers.
Traditionally, the most popular data analyst degrees are economics, engineering, and business. Getting a bachelor’s degree in a technical field, such as statistics, science, mathematics, engineering, computer science or IT. Internships in college in data analysis, data mining, or advanced machine learning courses may help you land a role directly as a data scientist. Learn more about the differences between a data analyst and data scientist here (COMING SOON!).
There is also substantial demand for analytical individuals that many companies will hire those who got educated via online courses. At JobStep, we recommend data analytics and SQL online courses from programs like Mode Analytics SQL School, Khan Academy, and SQLZOO.
Finally, some data analysts learn on the job when they are in a different department, such as sales, marketing, customer success, and revenue operations. If you are in one of these departments, you can build in your own experiences by identifying value-add analyses for your employer. That way you can work on developing your analytical skill sets while aligning that with your employer’s goals and objectives.
What experience should I have on a data analyst resume?
Employers usually like to see experience working in team-settings in previous roles and experience analyzing data with SQL and excel. Your experience doesn’t have to be from paid work experiences. You can demonstrate having worked with SQL in side projects or from taking online courses.
Need-to-have
- Experience with SQL
- Expert in Microsoft Excel and Google Sheets: specifically pivot tables, lookups, aggregating stats, and using formulas to run calculations
- Experience developing reports and dashboards utilizing BI visualization tools (Tableau, Microstrategy, Lookr, ggplot2, Power BI, etc.)
- 1-3 analysis projects that you can walk through end-to-end
Nice-to-have
- Python, R, or Java (usually 1 programming language is sufficient)
- 3+ analysis projects that you can walk through end-to-end
- Experience collaborating with other business departments such as sales, marketing, customer success, and revenue operations
- Project management experience
- Experience running AB Tests
- Experience running linear and logistic regressions
Above and Beyond
- Machine Learning experience
- Most companies may list other software, especially customer relationship management software (CRMs) on job descriptions. For the most part, these are nice-to-haves as long as you can show your ability to learn a new system quickly
- Using APIs to pull data or learning how to scrape data from 3rd party sources
- Strong customer service experience
- Building automated scripts to run analyses and send reports on a schedule
What skills should I have on a data analyst resume?
With employers screening for specific skills and tools, it is important to have a Skills section at the bottom of your resume. We advise that you also sprinkle the skills you have and tools you have used in your resume bullet points. An example of a well-written Skills section is below:
SKILLS
Data Analysis (SQL, Microsoft Excel), Data Visualization (Tableau), Database Storage (Microsoft Management Studio, Simphony POS system from Oracle), Business Metric Tracking (Chartbeat, Google Analytics, Google Data Studio), Coding Languages (C++, HTML/CSS, Java, JavaScript, Python), Microsoft PowerPoint, Google Suite
It is more than okay if you do not have all the skills in the example above. What is well-done in the example is the use of keywords to bucket the tools used. A data analyst will need to have skills in data analysis, data visualization, business metric tracking, and database storage. Listing these out and including the tools in parentheses allows you to showcase your skill set.
What do hiring managers look for in a data analyst resume?
Your ability to land a data analyst career should be determined by your data analysis skills, not your ability to write an optimized resume. With that, we would like to share what we call Mad Libs for Resume Bullets. You can start with the below bullet points and plug in details specific to your previous projects and experience.
Mad libs for resume bullets
- Solved [type of problem], driving [company impact].
- Prototyped [approach] model/tool using [technology] in order to [company or project goal]. Achieved [metric] performance.
- Forecasted [metric] for [team/clients].
- Created a dashboard to track KPIs and led department to process orders by X%.
- Developed pipeline to clean/label/extract [amount] of [data type] data.
- Drove X% increase in revenue by designing a new feature. Analyzed data, identified solution, and gained buy-in with senior leadership to secure resources.
- Drove X% increase in productivity by learning SQL, building new SQL database, and automating KPI reporting for the entire team.
General advice for creating a resume
Along with sharing some data analyst-specific advice, we also wanted to share some general best practices to keep in mind when creating a resume.
An effective resume bullet does three things efficiently:
- Use keywords from the job description for the role you are applying for.
- Summarize the most important goal that you accomplished. Ask yourself what project had the biggest impact on the company you worked for? Be sure to quantify as much as possible here.
- Highlight the job-relevant skill and personality traits you used to accomplish the goal.
Try your best to limit a large portion of resume bullets to 1 line. Too many multi-line resume bullets makes it hard for the employer and hiring manager to skim.
Other considerations to keep in mind when crafting your resume:
- Applicant Tracking System (ATS) filters cannot analyze fancy fonts, colors, charts, shapes, and designs. Only use the pretty version of your resume for networking, but never for online applications. Wondering how to beat ATS filters? We have a webinar walking you through how to Reverse Engineer the Applicant Tracking System here.
- Keywords are important to have in your resume, but not at the expense of clear, impactful language. It’s important for your resume to be skimmable.
- The keywords that matter are the technical skills and words that demonstrate you understand the industry context and users. Be sure to also highlight common frameworks, terms, and tools used in your role.
As a data analyst, you should talk about how you use SQL. If you want to one day move into data science, you should talk about how you’ve trained a model.
How to structure resume bullet points on a resume
There are a few ways that you can format a resume bullet point:
- [1st Verb] [Result] by [Action]
- [Verb] [Number] [Responsibility] per [Time Period]
- [Verb] [Business metric] by X % via ___ OR [Verb] [Business metric] from ___ to ____ by ___
Before
- Increased profitability across businesses by conducting revenue analyses.
- Reduced costs by conducting weekly analyses to identify suppliers with lower pricing for raw materials.
- Saved time by creating new dashboards to streamline administration tasks.
- Analyzed email campaigns.
After
- Grew net profits by 5% and 10% in Year 1 & Year 2 by coordinating 5 analysts to provide data insights to 7 managers at brick & mortar businesses.
- Reconciled $50,000 annual purchases, leading weekly reverse-bid negotiation to lower upstream supply chain cost, saving an average of $5,000 annually.
- Reduced administrative time by 300 hours/week by developing role-based Google Sheet databases and dashboards.
- Improved email subscription rates from 0.2% to 1% by using Google Data Studio & pulling data from SiteImprove to analyze livestream viewing audience metric and build recommendations
Looking for more resume support?
Our team at JobStep specializes in helping job seekers break into and build data analyst careers. JobStep gets you 5 interviews in 6 weeks by finding and applying to jobs that are great for you.
