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Data analyst

Collects and cleans data, computes metrics and builds the reports and dashboards a team decides by.

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What the work is

A data analyst answers business questions with numbers: why sales dropped, which customers are leaving, which promotion worked. They pull data from databases and spreadsheets, clean it, calculate metrics, build charts and dashboards, and explain the findings to the people who make decisions. The main tools are SQL, Excel, Python or R, and Power BI or similar reporting software.

A typical day

In the morning, a look at the key metrics on the dashboard for any sharp changes. Then a request from marketing: compare two ad campaigns. Queries to the database, cleaning the data, calculations, checking the figures against the source. Preparing a short conclusion with one or two charts and talking it through with the person who asked.

How to start

  • Get confident with Excel and learn SQL — they are the core of the job.
  • Learn basic statistics: averages, distributions, correlation, A/B tests.
  • Take an open dataset and do your own analysis with conclusions and charts.
  • Confirm Excel and SQL with tests on the platform and show the project in your portfolio.

Related professions

Financial analyst, economist, backend developer, marketer.

Skills by level

A beginner's route: the foundation first, then the core of the profession, then the advanced level. Each skill shows the level to aim for.

  1. Foundation

    where to start

    • Excelkey skilllevel: intermediate
    • Statisticskey skilllevel: intermediate
    • SQLkey skilllevel: intermediate
    • Data Analysiskey skilllevel: intermediate · after: Statistics, Excel
    6 free resources
  2. Core

    what every employer asks for

    • Pythonlevel: intermediate
    • Pandaslevel: intermediate · after: Python
    • Power BIlevel: intermediate · after: Data Visualization
    • Data Visualizationlevel: intermediate · after: Data Analysis
    5 free resources
  3. Advanced

    what sets a strong specialist apart

    • Tableaulevel: intermediate · after: Data Visualization
    • Machine Learninglevel: beginner · after: Pandas, Statistics
    • ETLlevel: beginner · after: SQL
    • Big Datalevel: beginner · after: ETL
    1 free resource

Key skill — the profession cannot do without it.

What it pays

Not enough data

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Vacancies published from Oct 2025 to Sep 2026

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