Data analysts collect, clean, and interpret data to help companies make better decisions. The daily reality: 60–70% data cleaning and preparation, 20% analysis and visualization, 10% presenting findings. Job descriptions often overstate technical requirements — most entry-level roles need SQL, Excel, and one BI tool (Tableau or Power BI). When a posting says "strong analytical skills," it means "can you find the story in a messy spreadsheet and explain it to someone who doesn't know SQL?"
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What does a data analyst do?
A data analyst collects, cleans, and interprets data to help organizations make evidence-based decisions. Daily tasks include writing SQL queries, building dashboards in Tableau or Power BI, cleaning messy datasets, and presenting findings to business stakeholders. About 60–70% of the work is data preparation (cleaning, joining, formatting), 20% is analysis and visualization, and 10% is communication and presentation.
What skills are required for a data analyst job?
SQL (required — appears in 90%+ of postings), Excel or Google Sheets (required), a BI tool like Tableau or Power BI (required for most roles), Python with pandas (increasingly expected, especially at tech companies), and basic statistics (distributions, correlation, hypothesis testing). Communication skills — the ability to explain data findings to non-technical audiences — are listed in nearly every posting and are consistently cited as the top differentiator by hiring managers.
What is the difference between a data analyst and a business analyst?
Data analysts query databases, build dashboards, and analyze data to find insights. Business analysts gather requirements, map business processes, and bridge communication between business stakeholders and technical teams. Data analysts spend most of their time in SQL and BI tools; business analysts spend most of their time in meetings, documents, and project management tools. The roles overlap at many companies, especially smaller ones.
The biggest disconnect in the data analyst job market isn't a skills gap — it's a communication gap. Job descriptions are written in corporate shorthand that means something completely different from what it says on the surface. "2+ years of experience" doesn't mean what most candidates think it means. "Proficiency in data modeling" at a startup means something radically different from the same phrase at an enterprise bank. Understanding this translation layer is the difference between applying strategically and wasting months on mismatched roles.
Most "what does a data analyst do?" articles list responsibilities from job postings. That's the polished version. Here's what the actual work looks like — the unglamorous daily reality that nobody puts in a job description.
- Data Analyst
A data analyst transforms raw data into actionable business insights. Using SQL, spreadsheets, BI tools, and sometimes Python, data analysts extract data from databases, clean and prepare it for analysis, identify trends and patterns, build visualizations, and communicate findings to stakeholders who make decisions based on those insights. The role sits at the intersection of technical skill and business communication.
The Real Day-to-Day
Morning (focused technical work)
- Write SQL queries to pull data for the weekly marketing performance report — join campaign data with revenue data across 3 tables
- Fix a broken dashboard: a source table changed column names overnight, and the VP's KPI dashboard is showing blanks
- Clean a CSV export from Salesforce — 15,000 rows with inconsistent date formats, duplicate entries, and missing values in the region field
- Answer a Slack message from the product manager: "Can you pull retention rates by user cohort for the last 6 months?"
Afternoon (communication and collaboration)
- Present quarterly churn analysis to the customer success team — the key finding: customers who don't complete onboarding within 7 days churn at 3x the rate
- Build a new Power BI report for the finance team showing revenue by product line, with drill-down by region and time period
- Attend a data quality meeting to discuss why the customer table has 2,000 duplicate records (the CRM import script has a bug)
- Document the logic behind a complex dashboard so the analyst who covers during vacation doesn't have to reverse-engineer everything
The data analyst job is 60–70% data cleaning, 20% analysis, and 10% communication. The glamorous part — finding insights and making recommendations — is a small fraction of the daily work. Candidates who enjoy the cleaning and preparation process (and understand why it matters) outperform those who see it as a chore to rush through.
Now that the reality is clear, here's how to translate the corporate language of job postings into what hiring managers actually expect.
This is the core reference for anyone applying to data analyst roles. Job postings use standardized language that sounds technical and specific but actually means something quite different in practice.
| What the Job Description Says | What They Actually Mean |
|---|---|
| Strong analytical skills | Can you look at a messy spreadsheet and figure out what's going on without being told exactly how? |
| Proficiency in SQL | Can you write JOINs, GROUP BYs, and basic window functions? You don't need to optimize database performance. |
| Experience with data visualization tools | Have you built dashboards in Tableau or Power BI? Bonus if they're published and we can see them. |
| 2+ years of experience | We want someone who isn't a total beginner. A strong portfolio and relevant projects count. |
| Bachelor's degree in a quantitative field | Preferred but not required. Bootcamp + portfolio often substitutes. We're really asking: can you think analytically? |
| Experience with large datasets | Can you write efficient queries against tables with 1M+ rows without crashing the system? |
| Self-starter | We don't have great documentation and the data infrastructure is a bit messy. You'll need to figure things out. |
| Detail-oriented | One wrong number in a report to the VP, and trust takes months to rebuild. Accuracy matters more than speed. |
| Cross-functional collaboration | You'll attend meetings with marketing, finance, product, and engineering. You need to speak their language. |
| Data storytelling ability | Can you explain a chart to someone who doesn't know what a LEFT JOIN is? That's the actual skill. |
| Experience with Python or R | Nice-to-have for most roles. At the entry level, basic pandas proficiency is sufficient. R is very rarely required. |
| Knowledge of statistical methods | Can you explain what a p-value means in business terms? Do you understand correlation isn't causation? That's enough for most roles. |
For the full picture — skills, education paths, certifications, and career progression — see How to Become a Data Analyst in 2026.
Job descriptions are wishlists, not checklists. "2+ years of experience" is flexible for candidates with strong portfolios. "Proficiency in SQL" means competent queries, not database administration. "Strong analytical skills" means business problem-solving, not advanced mathematics. Apply to any role where you meet 60–70% of the stated requirements.
The same job title means radically different things depending on where you work.
A "data analyst" at a 50-person startup does fundamentally different work than a "data analyst" at a Fortune 500 company. Understanding these differences prevents applying to roles that don't match what you're looking for.
| Factor | Startup (under 200) | Mid-Size (200–2,000) | Enterprise (2,000+) | Consulting |
|---|---|---|---|---|
| Typical title | Data Analyst (or 'Analytics Generalist') | Data Analyst | Data Analyst II, Business Intelligence Analyst | Associate Analyst, Consulting Analyst |
| Scope of work | Everything — reporting, dashboards, ad-hoc, basic data engineering | Defined domain — marketing, product, finance, or operations analytics | Narrow specialization — one type of report or dashboard in one business unit | Client-facing — different project every 2–3 months |
| Data infrastructure | Messy — Google Sheets, CSV exports, maybe a basic data warehouse | Decent — data warehouse (Snowflake/BigQuery), established BI tools | Mature — enterprise data warehouse, governed tables, established pipelines | Varies by client — you work with whatever they have |
| Tools emphasis | SQL, Python, Tableau, and whatever else is needed | SQL, Tableau or Power BI, some Python | SQL, enterprise BI (Power BI, Looker, SAP), Excel | Excel, PowerPoint, SQL, Tableau — strong presentation skills |
| Growth speed | Fast — wear many hats, rapid promotion if company grows | Moderate — clear career ladder, structured reviews | Slow — many layers, promotion requires committee approval | Fast skill growth — slow title growth |
| Mentorship | Minimal — you're often the only analyst | Available — senior analysts and managers on the team | Structured — training programs, buddy systems, formal mentorship | Learning by doing — sink or swim with client expectations |
Startup analyst roles offer breadth but lack structure. Enterprise roles offer depth but move slowly. Mid-size companies balance both. Consulting offers rapid skill development but limited analytical depth. Choose based on whether you value learning speed, work-life balance, or career structure.
Industry matters just as much as company size.
The same "data analyst" title looks different in tech, finance, healthcare, and retail. Each industry has domain-specific requirements that generic job postings don't make obvious.
| Industry | Unique Requirements | Domain Skills | Typical Salary Premium |
|---|---|---|---|
| Tech / SaaS | Event tracking (Amplitude, Mixpanel), experimentation (A/B testing), product metrics (DAU, retention, funnel conversion) | Product analytics, cohort analysis, growth metrics | +10–15% above average |
| Finance / Banking | Financial modeling, SEC compliance awareness, Bloomberg or FactSet experience, regulatory reporting | Risk analysis, portfolio analytics, variance reporting | +20–30% above average |
| Healthcare | HIPAA compliance, EHR systems (Epic, Cerner), clinical terminology, PHI data handling | Clinical outcomes, patient flow, readmission analysis | +15–25% above average |
| Retail / E-Commerce | Customer segmentation, inventory analysis, demand forecasting, marketing attribution | RFM analysis, promotional effectiveness, supply chain KPIs | +5–10% above average |
| Government / Nonprofit | Public data standards, grant reporting, transparency requirements | Census data, survey analysis, program evaluation | -10–20% below average (mission-driven compensation) |
For a detailed salary breakdown by experience level, location, and industry, see Data Analyst Salary Guide.
Industry specialization creates salary premiums of 10–30%. Finance and healthcare offer the highest pay but require domain-specific knowledge (financial modeling, HIPAA compliance). Tech offers the most interesting analytical problems (experimentation, product analytics) with above-average compensation. Choose an industry based on genuine interest — domain passion sustains career growth.
Knowing which skills are truly required — versus inflated in the job description — is critical for targeted preparation.
Job descriptions blur the line between what's actually required and what's aspirational. Here's the honest breakdown for 2026.
| Skill | True Requirement Level | Frequency in Postings | Entry-Level Expectation |
|---|---|---|---|
| SQL | Required — non-negotiable | 90%+ | JOINs, GROUP BY, subqueries, basic window functions |
| Excel / Google Sheets | Required — used daily | 75%+ | Pivot tables, VLOOKUP/INDEX-MATCH, conditional formatting |
| Tableau or Power BI | Required for most roles | 65%+ | Build dashboards from scratch, publish on Tableau Public |
| Python (pandas) | Nice-to-have at entry level | 40–55% | Import CSV, basic cleaning, simple visualizations |
| Statistics | Nice-to-have (basic level) | 45%+ | Mean, median, distributions, correlation — not regression modeling |
| R | Rarely required | 15% | Skip unless targeting biotech, academia, or pharmaceutical roles |
| Communication | Required — underestimated | 85%+ | Explain findings in plain English, write clear documentation |
| Data modeling | Usually not required for analysts | 20% | Understanding star schema is sufficient — building data models is engineering |
| Machine learning | Not required (data science skill) | 10% | Ignore for data analyst roles — focus on descriptive analytics |
For a complete skills breakdown with proficiency benchmarks and learning order, see Data Analyst Skills & Tools.
Three skills are truly required for data analyst roles: SQL, Excel, and one BI tool. Python is a strong differentiator but rarely a hard requirement at the entry level. Communication is listed in 85%+ of postings and is consistently the most underrated skill by candidates — it's what separates analysts who get promoted from those who don't.
Some job descriptions reveal more about the problems with a company than the problems they want solved.
Not every data analyst role is worth applying to. Some job descriptions contain warning signs that experienced analysts recognize immediately — but that career changers and new graduates often miss.
- "Data analyst / data scientist / data engineer" — three roles in one posting means the company doesn't understand what it needs and you'll be stretched impossibly thin
- "Must know SQL, Python, R, Tableau, Power BI, Looker, SAS, SPSS, Hadoop, and Spark" — an unrealistic tool list signals a copy-pasted JD written by someone who doesn't understand the role
- "Fast-paced environment" combined with "wear many hats" and no mention of team or mentorship — translation: you'll be the only analyst and there's no support
- No mention of data infrastructure, data warehouse, or BI tools — the company may not have data infrastructure, meaning you'll spend months building what should already exist
- "Competitive salary" with no range disclosed — often signals below-market pay and an unwillingness to be transparent
- "5+ years experience" listed as "entry-level" — either the posting is miscategorized or the company doesn't know what entry-level means
- Job duties list includes "manage the data warehouse" and "build ETL pipelines" — these are data engineering tasks, not analyst tasks
Green flags to look for:
- Specific BI tool mentioned (Tableau, Power BI, Looker) — the company has invested in analytics tooling
- Clear team structure mentioned ("join a team of 4 analysts reporting to the Analytics Manager") — indicates established analytics function
- Salary range disclosed — signals transparency and fair hiring practices
- Specific business problems mentioned ("analyze customer retention," "optimize marketing spend") — the role has defined scope
For complete application strategies, interview tips, and job search tactics for new data analysts, see Entry-Level Data Analyst Guide.
Red flags in job descriptions: a single role combining analyst/scientist/engineer, unrealistic tool lists (8+ tools), no mention of data infrastructure, and "5+ years experience" for "entry-level." Green flags: specific BI tools, clear team structure, salary transparency, and defined business problems.
- 01Data analysts spend 60–70% of their time on data cleaning, 20% on analysis, and 10% on communication — the reality is less glamorous than the job posting suggests
- 02Job descriptions are wishlists: '2+ years experience' is flexible, 'proficiency in SQL' means competent queries, and 'Bachelor's in quantitative field' often accepts bootcamp + portfolio
- 03Three skills are truly required: SQL, Excel, and one BI tool (Tableau or Power BI) — Python and statistics are strong differentiators but rarely hard requirements at entry level
- 04Company type dramatically changes the role: startup analysts do everything, enterprise analysts specialize narrowly, mid-size offers the best balance
- 05Industry specialization (finance, healthcare, tech) creates 10–30% salary premiums but requires domain-specific knowledge
- 06Red flag job descriptions list 8+ tools, combine analyst/scientist/engineer into one role, or don't mention data infrastructure — these signal dysfunctional analytics teams
What does a data analyst do on a daily basis?
A typical day: writing SQL queries to pull data for reports and dashboards, cleaning messy datasets from various sources, building and maintaining visualizations in Tableau or Power BI, answering ad-hoc questions from business stakeholders, and presenting findings in meetings. About 60–70% of the work is data preparation, 20% is analysis, and 10% is communication.
Is data analyst the same as business analyst?
No. Data analysts work primarily with data — writing SQL, building dashboards, and finding insights in datasets. Business analysts work primarily with people — gathering requirements, mapping processes, and bridging communication between business stakeholders and technical teams. There is overlap, especially at smaller companies, but the core tools and daily activities differ significantly.
What education do you need to be a data analyst?
A bachelor's degree in a quantitative field (statistics, mathematics, computer science, economics) is preferred at many companies but not universally required. About 65% of entry-level postings don't specify a required degree. Bootcamp certificates, self-taught portfolios, and professional certifications (like the Google Data Analytics Certificate) are increasingly accepted as alternatives.
Do data analysts need to know programming?
SQL is required — it's the primary language data analysts use to query databases. SQL is technically a query language, not a general-purpose programming language, but it's essential for every analyst role. Python is increasingly expected, especially at tech companies and for mid-to-senior roles, but the level required is data manipulation (pandas), not software engineering.
What is the difference between a data analyst and a data scientist?
Data analysts describe what happened and why (descriptive and diagnostic analytics) using SQL, dashboards, and reports. Data scientists predict what will happen (predictive and prescriptive analytics) using machine learning, statistical modeling, and experimentation. Data analysts need SQL, BI tools, and basic statistics; data scientists need Python/R, machine learning frameworks, and advanced mathematics.
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)
- 022024 Data Science and Analytics Hiring Survey — Burtch Works (2024)
- 03Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task — Forbes / CrowdFlower (2016)