Entry-level data analyst roles pay $55,000–$75,000 and roughly 65% of postings don't require a specific degree. The fastest path in: learn SQL + Excel + one BI tool, build 2–3 portfolio projects with real datasets, earn one certification (Google Data Analytics Certificate is the best ROI), and target mid-size companies where you'll get real analyst work — not just report-pulling. Most "entry-level" postings asking for 2+ years of experience will still interview candidates with a strong portfolio.
This article was researched and written by the Careery team — that helps land higher-paying jobs faster than ever! Learn more about Careery →
Can you get an entry-level data analyst job with no experience?
Yes. About 65% of entry-level data analyst postings don't require a specific degree. Employers care about demonstrated skills: a portfolio with 2–3 projects using real datasets, SQL proficiency, and one BI tool (Tableau or Power BI). Career changers from finance, operations, and marketing often break in because they already understand business context — the hardest skill to teach.
How much do entry-level data analysts make?
Entry-level data analysts in the United States earn $55,000–$75,000 per year. The Bureau of Labor Statistics reports a median salary of $65,000 for operations research and data analysis roles at the entry level. Analysts at tech companies and in high cost-of-living cities earn at the top of this range. Remote roles typically pay 10–15% less than equivalent on-site positions in major metros.
What skills do I need for an entry-level data analyst job?
SQL (required — 90%+ of postings list it), Excel or Google Sheets, one BI tool (Tableau or Power BI), and basic statistics. Python is listed in about 40% of entry-level postings but is rarely a hard requirement at the junior level. The soft skill that matters most: the ability to explain findings to non-technical stakeholders.
Every year, thousands of career changers stare at entry-level data analyst postings and think the same thing: "Entry-level but requires 2 years of experience — how does that work?" The secret is that most of those postings are wishlists, not requirements. Companies will interview candidates who can demonstrate skills, even without formal job titles to back them up.
The data analyst job market is one of the most accessible entry points in tech. The tools are free to learn, the proof of competence is a portfolio (not a pedigree), and the demand keeps growing. But "accessible" doesn't mean "easy" — the candidates who get hired understand what companies actually want versus what job postings say.
The gap between what people imagine and what the job actually entails kills more applications than any skills gap. Entry-level data analysts don't build machine learning models or architect data warehouses. They answer questions with data — and the questions are usually simpler than expected.
- Entry-Level Data Analyst
An entry-level data analyst (0–2 years of experience) writes SQL queries, builds dashboards in Tableau or Power BI, cleans messy datasets in Excel or Python, and presents findings to business stakeholders. The core function is translating data into actionable business recommendations. Most entry-level analysts spend 60–70% of their time on data cleaning and preparation, 20% on analysis, and 10% on presenting findings.
What the first year actually looks like:
- Pulling weekly and monthly reports that stakeholders rely on for decisions
- Cleaning CSVs and spreadsheets that arrive in inconsistent formats
- Building and maintaining dashboards that track KPIs
- Answering ad-hoc questions from managers ("How many users churned last quarter in the Southeast region?")
- Learning the company's data infrastructure, naming conventions, and business logic
Entry-level data analyst roles focus on reporting, dashboard building, and ad-hoc analysis — not machine learning or data engineering. The majority of the work is data cleaning and SQL queries. Hiring managers look for candidates who can answer business questions reliably, not candidates who know the fanciest tools.
The job postings, however, tell a different story. Understanding that gap is the key to getting hired.
Most entry-level job postings blend "must-haves" and "nice-to-haves" into a single bullet list. Knowing which is which saves months of misguided studying.
| Skill | Actual Requirement Level | How to Prove It |
|---|---|---|
| SQL | Required — non-negotiable | Portfolio project with JOINs, CTEs, window functions |
| Excel / Google Sheets | Required — used daily | Pivot tables, INDEX-MATCH, conditional formatting |
| Tableau or Power BI | Required — pick one | 2–3 published dashboards on Tableau Public |
| Python (pandas) | Nice-to-have at entry level | One portfolio project using pandas for cleaning |
| Statistics | Nice-to-have (basic level) | Understand mean, median, distributions, correlation |
| R | Rarely required at entry level | Skip unless targeting academic or biotech roles |
| Communication skills | Required — underestimated | README files, presentation slides in portfolio |
For the full skills breakdown and learning order, see How to Become a Data Analyst in 2026 — the complete guide covering education paths, skills, and career progression.
SQL, Excel, and one BI tool are true requirements for entry-level roles. Python, statistics, and R are nice-to-haves that strengthen applications but rarely gate you out. Spend 80% of preparation time on the required three.
But even "required skills" are only half the puzzle. The real barrier is understanding what job postings actually mean.
Entry-level data analyst postings are written in a language that sounds specific but means something entirely different in practice. This translation table is the most valuable thing a candidate can study before applying.
| What the Posting Says | What They Actually Mean |
|---|---|
| 2+ years of experience required | We want someone who isn't a complete beginner — a strong portfolio counts as experience |
| Proficiency in SQL | Can you write JOINs and GROUP BYs without Googling every syntax? That's proficient. |
| Experience with data visualization tools | Have you built at least 2 dashboards in Tableau or Power BI? Published examples are gold. |
| Strong communication skills | Can you explain a chart to a VP without using the word 'aggregation'? |
| Bachelor's degree in a related field | A degree is preferred but a bootcamp certificate + portfolio often substitutes |
| Experience with Python or R | Can you import a CSV into pandas and do basic cleaning? That's enough for entry-level. |
| Self-starter who thrives in ambiguity | We don't have great documentation and you'll need to figure things out yourself |
| Cross-functional collaboration | You'll attend a lot of meetings and need to understand what marketing and finance actually want |
| Familiarity with statistical methods | Do you know what a p-value is? Can you calculate a simple correlation? That's sufficient. |
| Experience with large datasets | Can you query a table with 1M+ rows without crashing the database? |
"Entry-level" in data analytics means "we'll train you on our systems, but you need to show up with foundational skills." A portfolio with real projects translates directly into the "experience" most postings demand. When a posting says "2 years experience," apply anyway if your portfolio demonstrates competence.
Now the question becomes: how do you build that experience when you haven't had the job yet?
The biggest Catch-22 in career changing: every job wants experience, but you need a job to get experience. The solution is building proof of competence outside of employment. Here are the highest-leverage ways to do it.
Portfolio Projects (Highest Impact)
Build 2–3 portfolio projects using real, messy datasets — not the cleaned Titanic or Iris datasets from Kaggle tutorials. Each project should include:
- A clear business question
- Data sourced from a public dataset (Census, WHO, NYC Open Data, Kaggle competitions)
- SQL queries or Python scripts for data cleaning
- A Tableau/Power BI dashboard with insights
- A written summary explaining what the data shows and what decisions it supports
Need specific project ideas with datasets and deliverables? See Data Analyst Portfolio Projects That Actually Get You Hired — with beginner, intermediate, and advanced projects.
Kaggle Competitions & Datasets
Kaggle isn't just for data scientists. The Datasets section has thousands of real-world datasets perfect for analyst-level projects. Join a competition in the "Getting Started" tier to practice working with unfamiliar data under constraints.
Volunteer & Pro Bono Work
Nonprofits, local businesses, and student organizations have data they don't know what to do with. Offer to build a dashboard for a local food bank's donation data or analyze survey results for a community organization. The work is real, the stakeholders are real, and the portfolio piece is legitimate.
Freelance Micro-Projects
Platforms like Upwork and Fiverr have entry-level data analysis gigs: cleaning spreadsheets, building simple dashboards, pulling data from APIs. The pay is low, but the experience is real and the client testimonials add credibility.
Portfolio projects built with real datasets, volunteer analytics work, and Kaggle competition participation all count as legitimate experience. Hiring managers evaluate the quality of your work, not whether someone paid you to do it.
With projects in hand, a strategic certification can accelerate applications even further.
The certification market for data analysts is crowded and mostly useless. A handful of certifications carry real weight with hiring managers — the rest are resume filler.
| Certification | Cost | Time | Hiring Manager Perception |
|---|---|---|---|
| Google Data Analytics Professional Certificate | $49/mo (Coursera) | 3–6 months | Strong — widely recognized, good portfolio project included |
| IBM Data Analyst Professional Certificate | $49/mo (Coursera) | 4–6 months | Moderate — less recognized than Google but solid curriculum |
| Tableau Desktop Specialist | $100 exam | 2–4 weeks prep | Strong for roles requiring Tableau specifically |
| Microsoft Power BI Data Analyst (PL-300) | $165 exam | 4–6 weeks prep | Strong for Microsoft ecosystem companies |
| CompTIA Data+ | $392 exam | 4–6 weeks prep | Moderate — more theoretical, less practical |
For a full comparison of every relevant certification, costs, and which employers value them most, see Best Data Analyst Certifications.
The honest truth: Certifications open doors when paired with a portfolio. Alone, they prove you completed a course — not that you can do the work. The Google certificate is the best starting point because it includes a capstone project that doubles as a portfolio piece.
The Google Data Analytics Certificate is the highest-ROI certification for entry-level candidates. Pair any certification with 2–3 portfolio projects for maximum impact. Certifications alone don't get you hired — they get your resume past the initial screen.
Where you apply matters as much as how qualified you are.
Not all entry-level roles are created equal. Company type determines what you'll actually do, how fast you'll grow, and how competitive the application pool is.
| Company Type | Pros | Cons | Best For |
|---|---|---|---|
| Startups (under 200 people) | Wear many hats, fast learning, visible impact | Chaotic data, minimal mentorship, unstable | Self-starters comfortable with ambiguity |
| Mid-size (200–2,000 people) | Structured teams, real analytics work, mentorship available | Slower hiring process, some bureaucracy | Best balance for first analytics role |
| Enterprise (2,000+) | Training programs, established data infrastructure, higher salaries | Narrow scope, slow projects, more politics | Candidates who want structure and stability |
| Consulting firms | Exposure to many industries, fast skill development | Long hours, less depth, client-driven priorities | Fast learners who want variety |
| Nonprofits & Government | Mission-driven work, lower competition | Lower pay, outdated tools, slow processes | Career changers building first portfolio |
The mid-size sweet spot: Companies with 200–2,000 employees typically have enough data infrastructure to do real analytics work but are small enough that entry-level analysts get exposure to the full analytics lifecycle — from data cleaning to stakeholder presentations.
Mid-size companies (200–2,000 employees) offer the best entry point: structured enough for mentorship, small enough for broad exposure. Apply to 50–75 positions across company types, but weight mid-size companies heavily.
Getting the interview is one battle. Passing it is another.
Entry-level data analyst interviews typically follow a three-stage pattern: a recruiter screen, a technical assessment, and a case study or stakeholder interview. Each stage tests something different.
Stage 1: Recruiter Screen (15–30 minutes)
- Explain your background and why data analytics (have a concise story)
- Show enthusiasm for the specific company and role
- Name the tools you know and how you've used them
Stage 2: Technical Assessment (45–60 minutes)
- SQL questions: JOINs, GROUP BY, window functions, CTEs
- Excel or Sheets: pivot tables, formulas, data cleaning
- A take-home assignment with a real dataset is common
Stage 3: Case Study / Stakeholder Interview (30–45 minutes)
- Given a business question and a dataset, walk through your analysis approach
- Explain findings to a non-technical audience
- Demonstrate that you think about business impact, not just technical correctness
[Action verb] + [what you analyzed] + [tool used] + [business impact or result] Examples: - Analyzed customer churn patterns using SQL and Tableau, identifying 3 retention opportunities that informed the Q3 marketing strategy - Cleaned and standardized 15,000+ rows of vendor data in Python (pandas), reducing report generation time by 40% - Built a weekly KPI dashboard in Power BI tracking 12 metrics across 4 departments, adopted by the leadership team for quarterly reviews
For a complete list of technical and behavioral questions with sample answers, see Data Analyst Interview Questions.
Entry-level interviews test SQL competence, data cleaning ability, and communication skills. Prepare a 2-minute career story, practice SQL problems daily for 2 weeks before interviews, and always frame findings in business terms — not technical jargon.
Even strong candidates sabotage themselves with avoidable mistakes.
For resume templates, bullet formulas, and ATS optimization tips specific to data analysts, see Data Analyst Resume Guide.
- 01Entry-level data analyst roles pay $55K–$75K and most don't require a specific degree — skills and portfolio matter more than credentials
- 02Master SQL, Excel, and one BI tool (Tableau or Power BI) before anything else — these three skills cover the requirements of 80%+ of entry-level postings
- 03Build 2–3 portfolio projects with real datasets (not Titanic or Iris) that demonstrate data cleaning, analysis, and business recommendations
- 04The Google Data Analytics Certificate is the highest-ROI certification — it includes a capstone project that doubles as a portfolio piece
- 05Target mid-size companies (200–2,000 employees) for the best combination of mentorship, structured data, and broad exposure
- 06Apply to roles asking for 0–3 years of experience — 'required experience' in entry-level postings is usually a preference, not a gate
How long does it take to become job-ready as an entry-level data analyst?
With focused study: 3–6 months full-time, 6–9 months part-time. The core skills (SQL + Excel + one BI tool) take 8–12 weeks. Building 2–3 portfolio projects adds another 4–6 weeks. A certification like the Google Data Analytics Certificate takes 3–6 months but can overlap with skills learning.
Do I need a degree to get an entry-level data analyst job?
A degree is preferred but not required at most companies. About 65% of entry-level data analyst postings don't specify a required degree field. A strong portfolio, relevant certification, and demonstrated SQL proficiency can substitute for a degree at many employers — especially startups and mid-size companies.
What's the best entry-level data analyst certification?
The Google Data Analytics Professional Certificate on Coursera. It costs $49/month, takes 3–6 months to complete, and includes a capstone project that becomes a portfolio piece. It's the most widely recognized entry-level analytics certification among hiring managers.
Should I learn Python or SQL first for data analysis?
SQL first — always. SQL appears in 90%+ of data analyst job postings compared to ~40% for Python at the entry level. Most entry-level analysts use SQL daily and Python occasionally. Master SQL in 4–6 weeks, then learn Python after you've built your first portfolio projects.
How many jobs should I apply to?
Target 50–75 applications across company types over 4–6 weeks. Customize your resume for 3–4 role categories (startup analyst, enterprise analyst, industry-specific roles) rather than customizing for each individual posting. Quality applications with tailored bullets outperform mass applications with generic resumes.
Prepared by Careery Team
Researching Job Market & Building AI Tools for careerists · since December 2020
- 01Occupational Outlook Handbook: Data Analysts and Scientists — Bureau of Labor Statistics (2025)
- 02Google Data Analytics Professional Certificate — Google / Coursera (2025)
- 03Storytelling with Data: A Data Visualization Guide for Business Professionals — Cole Nussbaumer Knaflic (2015)