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Data Analyst

Data analysts turn vague business questions into answers other people act on. This page covers what the job actually involves, what it pays, how it differs from analytics engineering and data science, and the honest picture of which parts of it AI already does well.

Typical Pay (US)*

$85kmedian*
$62k*
Entry
$85k*
Median
$115k*
Senior

* AI-estimated from general U.S. labor-market patterns — not measured data from the U.S. Bureau of Labor Statistics or any official source. Real pay varies widely by location, employer, experience, and timing.

Outlook

Stable demand*High AI exposure

This is the most AI-exposed job in the data cluster, and pretending otherwise does you no favors: "write a SQL query and make a chart" is precisely what current models do well, and the routine ad-hoc request queue is already shrinking at companies with a clean warehouse. What survives is the part that was never really about SQL — knowing which question is worth asking, understanding how the data was collected and where it quietly lies, designing an experiment that can actually answer something, and being trusted in the room where the decision gets made. Demand for the title stays roughly flat while the floor rises; the durable moves are up into analytics engineering or data science, not sideways into faster dashboards.

What does a Data Analyst do?

A data analyst answers questions about a business using data that already exists. A product manager asks why signups dropped 8% last week. You pull the funnel from the warehouse in SQL, split it by acquisition channel and platform, find that iOS signups are flat and Android fell off a cliff, check the release timeline, and tell them a Play Store rollout on Tuesday is the likely culprit. Then you build a dashboard so nobody has to ask you again. That loop — question, query, chart, explanation, artifact — is the job. Most of your hours go to SQL, a BI tool (Looker, Tableau, Power BI, Metabase, or Mode), a spreadsheet, and the Slack conversation where you explain what the number means.

The title gets confused with four neighbors, and the boundaries are real. An analytics engineer builds the modeled, tested tables you query — they own dbt models, dimensional models, and the semantic layer, and they treat SQL as software with version control and tests. A data engineer builds the pipelines that land raw data in the warehouse at all: ingestion, orchestration, streaming, cost, uptime. A data scientist owns statistical inference — experiment design, causal analysis, and predictive models — rather than reporting. A business analyst works on process and requirements: mapping how an order actually flows through four systems and writing the spec for the fix. You sit downstream of the engineers and upstream of the decision.

The federal occupational taxonomy has not caught up. U.S. O*NET 28.3 has no "Data Analyst" occupation at all — the nearest matches are 15-2041 Statisticians and 15-2031 Operations Research Analysts, and "Data Analyst" appears only as a listed alias under 15-2051 Data Scientists. That gap is worth knowing when you read salary surveys, because a single job title spans a marketing analyst pulling campaign reports in Excel and a product analyst designing A/B tests at a tech company. The pay ranges for those two jobs barely overlap.

The role suits people who are genuinely curious about how a business works and who like being the person who ends an argument with evidence. It does not suit people who want to build systems — you will spend more time in conversations than in an editor, and the code you write is mostly disposable. It is also the most common entry point into the data field, which is both its strength and its risk: the bottom of the role is crowded and increasingly automatable, and the analysts who thrive are the ones who move up the question, not just the query.

A day in the life

  • Clear an overnight Slack queue of ad-hoc requests, triaging which three actually change a decision this week
  • Write and debug a 120-line SQL query joining subscription events to marketing attribution, then discover the join duplicates rows and fix the grain
  • Rebuild a Looker dashboard after a dbt model rename breaks four tiles, and add a definition note so the next person understands the metric
  • Sit in a weekly business review and explain, out loud, why retention looks worse than last month and which part is seasonality
  • Pull a cohort table for a pricing experiment and push back on the PM's readout because the test hasn't reached the sample size it needs
  • Audit a metric two teams disagree about, trace both definitions back to source tables, and write the one-paragraph decision on which is correct
  • Spend the last hour of the day documenting a recurring report so it becomes a scheduled query instead of a standing ask

How to become a Data Analyst

  1. 1

    Get genuinely good at SQL

    ~2 months

    Go past SELECT and JOIN into window functions, CTEs, grain and fan-out bugs, and date logic. This single skill carries most of the job and most of the interview.

  2. 2

    Learn one BI tool and one spreadsheet deeply

    ~1 month

    Pick Tableau, Looker, Power BI, or Metabase and build real dashboards in it. Excel or Sheets pivot tables still decide more meetings than any BI license.

  3. 3

    Add enough statistics to avoid being wrong confidently

    ~2 months

    Distributions, sampling, confidence intervals, and the difference between correlation and a causal claim. You need enough to catch a bad experiment readout, not a graduate degree.

  4. 4

    Build two portfolio analyses on messy public data

    ~2 months

    Not Titanic. Take a real messy source, state a business question, document the cleaning decisions you made, and end with a recommendation someone could act on.

  5. 5

    Get inside a business, in any seat

    ~3 months

    Operations, support, marketing, or finance roles that touch reporting are the most reliable side door. Domain context is what separates a hired analyst from a rejected one.

  6. 6

    Pick your upgrade path before you plateau

    ~12 months in

    Within two years, decide whether you are heading toward analytics engineering (dbt, testing, modeling) or data science (experimentation, causal inference). The generalist middle is the exposed part.

Skills that matter

SQL (window functions, CTEs, query debugging)Dimensional data modeling basics (facts, dimensions, grain)A BI tool: Looker, Tableau, Power BI, or MetabaseExcel / Google Sheets pivot tables and lookupsPython with pandas for work SQL can't doDescriptive statistics and samplingA/B test reading: sample size, significance, novelty effectsMetric definition and documentationData quality auditing and source-of-truth tracingChart design and choosing the right visual encodingWritten analysis: a one-paragraph recommendation, not a wall of chartsStakeholder question refinement — turning a vague ask into a testable one

Learn the actual skills

Mochivia's structured roadmap walks you from fundamentals to job-ready — 15 minutes a day.

See the Roadmap

Frequently asked questions

What does a data analyst do?
A data analyst answers business questions using data that already exists. The core loop is taking a question like "why did conversion drop last week," writing SQL against the company's data warehouse, checking the result for data-quality problems, building a chart or dashboard, and explaining what it means to the people making the decision. Most analysts spend their time in SQL, a BI tool such as Looker or Tableau, and spreadsheets. The job is roughly half technical and half communication.
How much do data analysts make?
In the United States, data analysts typically earn around $62,000 entering the field, about $85,000 at mid-level, and roughly $115,000 in senior positions. These are AI-generated estimates, not measured Bureau of Labor Statistics figures, because O*NET has no dedicated "Data Analyst" occupation code. Pay varies widely by industry — a product analyst at a technology company can earn well above a marketing analyst at a similar level of experience, and location still moves the number by 20% or more.
Can you become a data analyst without a degree?
Yes, and it is one of the more realistic degree-optional paths in tech, though it is harder than it was in 2021. Employers care about demonstrated SQL ability, two or three portfolio analyses on genuinely messy data, and domain knowledge of their business. The most reliable route without a degree is entering through an adjacent operations, support, or marketing role that already touches reporting, then moving internally. Certificates alone rarely get interviews; a portfolio plus internal credibility does.
Will AI replace data analysts?
AI will not eliminate the role, but it is already absorbing the most common version of it. Writing a SQL query and producing a chart from a clear prompt is something current models do well, so the routine ad-hoc request queue is shrinking at companies with clean, well-modeled data. What does not automate is knowing which question is worth asking, understanding how the data was collected and where it misleads, designing a valid experiment, and holding credibility in the room where the decision happens. Analysts who stay at query-writing are exposed; analysts who own the question are not.
Is data analyst a good career in 2026?
It is a good entry point and a risky endpoint. Demand for the title is roughly flat rather than growing, the junior end of the market is crowded, and the mechanical parts of the work are the most automatable in the data field. It remains valuable as a first rung because it teaches you the business and the data at the same time — but plan an upgrade path into analytics engineering or data science within about two years rather than treating it as a destination.
What is the difference between a data analyst and an analytics engineer?
A data analyst consumes tables to answer business questions; an analytics engineer builds and tests the tables analysts consume. The analytics engineer works in dbt, owns dimensional models and the semantic layer, and applies software practices — version control, testing, code review, CI — to SQL. The analyst works in BI tools and conversations. Analytics engineering typically pays $25,000 to $35,000 more at equivalent seniority, and the most common way into it is a data analyst who learned engineering discipline.

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