Do I need a degree in data science or analytics to get hired?

No. Many data analysts have degrees in unrelated fields. The military counts as experience. A portfolio project and demonstrable SQL and Excel skills matter more than a specific degree for most entry level analytics roles. If you want to move into data science later, that path usually requires a degree or advanced training. But data analyst, business intelligence analyst, and reporting analyst roles are accessible with experience and the right tools on your resume.

What is the difference between a data analyst and a business intelligence analyst?

The titles overlap significantly and the difference varies by company. Generally, a data analyst works with data to answer specific business questions, often using SQL and Excel. A business intelligence analyst focuses more on dashboards, reporting systems, and making data accessible to decision makers, often using Tableau or Power BI. Both roles value SQL and the ability to communicate findings clearly. Many job postings use the titles interchangeably.

How much do entry level data analysts make?

Entry level data analyst salaries range from $55,000 to $80,000 depending on location, industry, and the specific tools required. Business intelligence analysts start slightly higher, often $65,000 to $90,000. Veterans with security clearances and analytics skills can command a premium in defense contracting and government consulting roles, where cleared data analysts often start at $80,000 to $100,000.

I do not have SQL on my resume yet. Should I wait to apply until I learn it?

Apply now and learn SQL this week. You can get through the basics of SELECT, WHERE, JOIN, and GROUP BY in three to five days of focused practice. Start the learning today. Apply for jobs today. By the time you get an interview, you will have a working knowledge of SQL. You do not need to be an expert to get hired. You need to be able to answer basic SQL questions in a technical screen.

Tailor Your Resume for Data Analytics, Business Intelligence, and Tech Operations Jobs

By GetVetsHired · August 2026 · 5 min read

You have been working with data for years. Readiness numbers. Maintenance reports. Personnel accountability. Supply chain metrics. Budget tracking. You may not have had the title 'data analyst' but you were doing the work. The challenge is getting a civilian hiring manager to see it. Your resume calls it 'tracked unit readiness statistics' and the employer is looking for someone who 'analyzes operational data to drive business decisions.' Same thing. Different words. Here is how to close that gap and build a resume that data and analytics hiring managers actually respond to.

You have more data experience than you think

If you were an NCO or officer in any branch of the military, you managed data. You pulled reports. You tracked metrics. You briefed commanders on numbers that informed decisions about personnel, equipment, and operations. You may have built spreadsheets to track training completion across your unit. You may have monitored supply levels and forecasted shortages. You may have analyzed maintenance trends to predict vehicle downtime. None of this was called data analytics. All of it was data analytics. The first step to building a data focused resume is recognizing that you have been doing this work for years. Once you see it, the resume writes itself.

How to translate military data work into civilian analytics language

Here are the most common military data experiences and how to express them on a civilian resume. 'Tracked unit training readiness and reported metrics to battalion commander' becomes 'Monitored team performance metrics, analyzed completion rates, and delivered weekly reports to senior leadership.' 'Managed equipment maintenance schedules and predicted parts requirements for 50 vehicles' becomes 'Analyzed maintenance data to forecast inventory needs and reduce equipment downtime across a fleet of 50 assets.' 'Built Excel trackers for personnel accountability during deployments' becomes 'Designed and maintained personnel data tracking systems supporting a 200 person organization during high tempo operations.' 'Reviewed after action reports and compiled lessons learned for command review' becomes 'Analyzed operational outcomes, identified patterns in performance data, and delivered actionable recommendations to executive leadership.' Notice the pattern. You did the work. The military gave it a military name. The civilian version uses the same verbs with different nouns. Data is data. Analysis is analysis. The translation is mostly vocabulary.

The tools you need on your resume (and the ones you can learn quickly)

Data analytics hiring managers look for specific tools. Here is what they want to see and what you need to know about each one. SQL: this is the non negotiable one. SQL is how you query databases. If you have never used it, you cannot fake it. But you can learn the basics in two to three weeks using free resources like Mode Analytics SQL tutorial or SQLZoo. Put it on your resume after you can write basic SELECT, JOIN, and GROUP BY queries. Excel: you almost certainly already know Excel. The hiring manager wants to see pivot tables, VLOOKUP, and basic formulas. If you built spreadsheets in the military, you have this skill. Say so. Tableau or Power BI: these are data visualization tools. They turn numbers into charts and dashboards. Both have free versions and both can be learned in a weekend. One decent dashboard project on your resume shows you can use the tool. Python or R: these are nice to have but not required for entry level analytics roles. If you know them, great. If you do not, focus on SQL and Excel first. The tool stack for a junior analytics role is SQL plus Excel plus one visualization tool. That is achievable for a transitioning veteran in under a month of focused study.

How to structure a data analytics resume when you have no analytics job title

Your resume should lead with the data work, not the military context. Most veterans make the mistake of burying their analytics experience under paragraphs of operational detail. The hiring manager has to dig to find it and they will not. Here is the fix. In your summary, lead with the data angle. 'Operations professional with five years of experience analyzing performance metrics, building data tracking systems, and delivering reports to senior leadership. Transitioning to a data analytics role. Proficient in Excel, SQL, and Tableau.' That summary tells the hiring manager immediately that you are a data person who happens to have a military background, not a military person who is trying to pivot. In your experience bullets, put the data and analysis bullets first. Lead every entry with the measurable, quantifiable, analytical work you did. The operational context goes second. The hiring manager reads your resume in roughly six seconds. The first bullet of every role is the one that gets read. Make it about data.

Build a portfolio project that proves you can do the work

Civilian career changers into data analytics build a portfolio of projects. You can and should do the same. It does not need to be complex. One solid project is better than three half finished ones. Here is an idea that takes one weekend. Pick a public dataset. Government data is free and abundant. The Bureau of Labor Statistics has employment data. The VA publishes veteran population data. Grab a CSV, load it into Excel or Tableau, find something interesting, and build a one page dashboard or a short slide deck showing your findings. Then put a link to that project on your resume. Put it in your LinkedIn profile. Talk about it in interviews. This project proves two things. You can use the tools. And you care enough about this career change to do work nobody paid you to do. That second one is what separates candidates who get hired from candidates who stay in the pile.

The interview: how to talk about your data experience without military jargon

In the interview, you will be asked about your data experience. Do not start with 'I was a squad leader in the 82nd.' Start with what you analyzed and what happened because of it. 'I tracked training completion across a 120 person organization. I built a tracking system in Excel that flagged units falling behind schedule, which let us address gaps two weeks before the deadline instead of the day before. Completion rates improved from 82 percent to 96 percent in three months.' That answer works in any interview room. It has scope, action, and result. It uses tools the interviewer recognizes. It tells a story that any hiring manager understands. When the interviewer follows up with 'what did your job title actually mean,' then you can explain the military context. Lead with the data. Explain the context when asked.

The fastest way to get your data analytics resume ready

You can spend a weekend manually translating every bullet on your resume into data analytics language, cross referencing job descriptions for the right keywords, and formatting everything for an ATS. Or you can use a resume tailoring tool that does the heavy translation work for you. Upload your military resume and a data analyst job description. The tool pulls the analytics keywords from the job posting, translates your military experience into matching civilian language, and gives you a resume that passes the keyword screen. You still need to review it, edit it to your voice, and add any tools or projects you have built. But the time it saves on translation is real. The difference between 'tracked readiness numbers' and 'analyzed performance metrics' is one word change to you and a completely different signal to a hiring manager. Get the signal right.

Tailor My Resume for Data Analytics

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