How to become a Data Analyst

Data & AnalyticsAI-resilience: 78/100

Overview

Turn business questions into clean, trustworthy data work — the person who turns raw tables into a decision the team can defend.

Every function now runs on dashboards, and the analyst who pairs SQL fluency with sound judgement about what a number actually means is harder to replace than the tools around them. The BLS still projects 21% growth for Operations Research Analysts (2024–34) and the WEF Future of Jobs 2025 lists analytical thinking among the fastest-rising core skills.

What AI changes

What AI accelerates

First-draft SQL, ad-hoc summaries, dashboard scaffolding, and turning a vague question into a starter dataset.

What stays human

Defining the metric, choosing the right denominator, spotting the confound, and translating the finding into a decision the business will actually make.

AI drafts the SQL, writes the first version of the chart, and summarises what changed — but the analyst's value is in framing the question, defending the metric, and explaining to a non-technical leader what the number does and does not say. That judgement compounds; the routine parts of the job get faster and the analytical spine gets more valuable.

Day to day

Pull and shape data from the warehouse, write the SQL the dashboard depends on, QA numbers before they ship, build or update BI reports, and meet with stakeholders to clarify what they actually need to know.

Core skills

SQL & data queryingWriting joins, window functions, and CTEs that answer a real question efficiently on a real schema.
Spreadsheet modellingBuilding XLOOKUP-driven, version-controlled sheets that an analyst can actually audit six months later.
Data visualisationChoosing the chart that matches the question and removing ink until the comparison is obvious.
Statistical reasoningDistinguishing signal from noise, picking the right test or model, and being honest about uncertainty.
Stakeholder translationReading a chart or a table and knowing what it does and does not actually say, including the missing denominator.

Tools

SQL (Postgres, BigQuery, Snowflake)
Python (pandas)
Tableau, Power BI, or Looker
Excel/Google Sheets
dbt (basic)

How to get in

Entry routes

From a business, marketing, or finance analyst role
From a university stats/economics/computer science degree
From a SQL-focused data engineering or operations role

Certifications

Google Data Analytics
Microsoft Certified: Data Analyst Associate
Tableau Desktop Specialist

Seniority ladder

Level	Title	Experience	Focus	Salary
Entry	Junior Data Analyst	0–2 yrs	Writing SQL, building dashboards, learning the business	Entry of the US band, below the role median
Mid	Data Analyst	2–4 yrs	Owning reports end-to-end, partnering with stakeholders	Around the role median
Senior/Lead	Senior Data Analyst	4–7 yrs	Designing metrics, leading analyses, mentoring	Upper end of the US band
Director	Director of Analytics	7+ yrs	Analytics strategy, team leadership, cross-function data quality	Above the analyst band, with a management premium

Where it can lead

Progresses to

Senior Data Analyst
Analytics Manager
data-scientist
marketing-analytics-lead

Pivots to

business-analyst
product-analyst
revenue-operations-analyst
data-engineer

Pay (US)

Low

USD 70,000

Median

USD 91,290

High

USD 115,000

Closest BLS occupation: Operations Research Analysts (median $91,290, May 2024); data analyst bands sit around that median, higher in tech/major metros.

Indicative, sourced — not a guarantee.

Outlook

Data analyst demand is structurally strong; US Operations Research Analyst employment is projected to grow 21% (2024–34), much faster than the 3% all-occupation average.

Prove it

SQL Data Quality Audit on a Public Dataset
Effort: weekend
Churn Driver Analysis Memo
Effort: 1-2 weeks
A/B Test Write-Up From a Public Experiment
Effort: 1-2 weeks
Small ETL/Data Pipeline Repo
Effort: 1-2 weeks
Procurement Scorecard for One Category
Effort: 1-2 weeks
Supply Chain Reorder-Point Model
Effort: 1-2 weeks
3-Statement Financial Model (Public Co.)
Effort: 1 month
Budget Variance Analysis on a Public Budget
Effort: 1-2 weeks
Customer Health Score Model
Effort: 1-2 weeks
dbt Project From Raw to Mart
Effort: 1-2 weeks
Feature Flag Rollout Plan
Effort: weekend
Marketing Attribution Model From Scratch
Effort: 1-2 weeks

Interview prep

Walk me through how you'd approach a dataset you've never seen before.

Starts with understanding the business context and the question being asked, then checks data quality, missing values, and distributions before any modeling. Names specific tools or queries used.

Tell me about a time your analysis changed a business decision.

Describes the original assumption, what the data revealed, and the concrete outcome. Includes how you communicated the insight to non-technical stakeholders.

+1 more questions available with a personalised guide.

Your path into Data Analyst

See how your experience lines up — skill gaps, salary fit, and a personalised seniority match. No invented claims, just your real career mapped against this role.

Pro

Unlock all 10 career paths + deep reports

See full fit breakdowns, skill-gap maps, proof-project ideas, and salary outlooks for every path.

Pro for $29/mo.

See if your CV is fit for this

Sources

Overview

Turn business questions into clean, trustworthy data work — the person who turns raw tables into a decision the team can defend.

What AI changes

What AI accelerates

First-draft SQL, ad-hoc summaries, dashboard scaffolding, and turning a vague question into a starter dataset.

What stays human

Defining the metric, choosing the right denominator, spotting the confound, and translating the finding into a decision the business will actually make.

Core skills

SQL & data queryingWriting joins, window functions, and CTEs that answer a real question efficiently on a real schema.

Spreadsheet modellingBuilding XLOOKUP-driven, version-controlled sheets that an analyst can actually audit six months later.

Data visualisationChoosing the chart that matches the question and removing ink until the comparison is obvious.

Statistical reasoningDistinguishing signal from noise, picking the right test or model, and being honest about uncertainty.

Stakeholder translationReading a chart or a table and knowing what it does and does not actually say, including the missing denominator.

Seniority ladder

Level	Title	Experience	Focus	Salary
Entry	Junior Data Analyst	0–2 yrs	Writing SQL, building dashboards, learning the business	Entry of the US band, below the role median
Mid	Data Analyst	2–4 yrs	Owning reports end-to-end, partnering with stakeholders	Around the role median
Senior/Lead	Senior Data Analyst	4–7 yrs	Designing metrics, leading analyses, mentoring	Upper end of the US band
Director	Director of Analytics	7+ yrs	Analytics strategy, team leadership, cross-function data quality	Above the analyst band, with a management premium

Prove it

SQL Data Quality Audit on a Public Dataset

Effort: weekend

Churn Driver Analysis Memo

Effort: 1-2 weeks

A/B Test Write-Up From a Public Experiment

Effort: 1-2 weeks

Small ETL/Data Pipeline Repo

Effort: 1-2 weeks

Procurement Scorecard for One Category

Effort: 1-2 weeks

Supply Chain Reorder-Point Model

Effort: 1-2 weeks

3-Statement Financial Model (Public Co.)

Effort: 1 month

Budget Variance Analysis on a Public Budget

Effort: 1-2 weeks

Customer Health Score Model

Effort: 1-2 weeks

dbt Project From Raw to Mart

Effort: 1-2 weeks

Feature Flag Rollout Plan

Effort: weekend

Marketing Attribution Model From Scratch

Effort: 1-2 weeks

Interview prep

Walk me through how you'd approach a dataset you've never seen before.

Starts with understanding the business context and the question being asked, then checks data quality, missing values, and distributions before any modeling. Names specific tools or queries used.

Tell me about a time your analysis changed a business decision.

Describes the original assumption, what the data revealed, and the concrete outcome. Includes how you communicated the insight to non-technical stakeholders.

+1 more questions available with a personalised guide.

Your path into Data Analyst

See how your experience lines up — skill gaps, salary fit, and a personalised seniority match. No invented claims, just your real career mapped against this role.

Pro

Unlock all 10 career paths + deep reports

See full fit breakdowns, skill-gap maps, proof-project ideas, and salary outlooks for every path.

Pro for $29/mo.