Data analyst: what they do, how much they earn, and how to become one

The data analyst is the professional who turns raw data into information that helps the company decide. In practice, they are the person who takes numbers scattered across spreadsheets, systems, and reports, organizes them, cross-references them, and delivers a clear answer to a business question. How much is this customer worth, why did margin drop, which supplier is late the most, where is inventory sitting idle. It is one of the fastest-growing careers in Brazil, and also one of those that raises the most questions for people who want to enter the data field without knowing where to start.

Anyone who follows my work knows I tend to say one thing: good data analysis starts long before the chart. It starts with the master record. And that is exactly why this profession is so interesting and, at the same time, so misunderstood. I will explain what the data analyst really does, how much they earn, which tools they use, and how you can become one.

What a data analyst does in practice

The data analyst's job is to answer business questions using data. It sounds simple, but that is where the complexity lives. They start by understanding what the area really needs to know. Then they go after the data, which is almost never ready. They extract, clean, join different databases, check that it makes sense, and only then analyze. In the end, they deliver in a format anyone can understand, usually a dashboard, a report, or a presentation.

A typical day involves writing SQL queries to pull information from a database, processing that data to remove duplicates and errors, building a dashboard in Power BI, and talking to the area that requested the analysis to explain what the numbers are saying. What few people realize is that a good part of the analyst's time is not spent analyzing. It is spent fixing data. And that happens a lot in companies because the data arrives messy.

I have seen competent analysts deliver a wrong analysis through no fault of their own. The purchasing report showed three different suppliers when, in fact, it was the same supplier registered in three ways. The math added up, the chart looked nice, and the decision came out crooked. The problem was not in the analysis. It was in the master record nobody governed.

Analyst, scientist, and data engineer: what is the difference

This is the most common question for those just starting out, and it is worth separating them well. The three careers work with data, but they solve different problems.

The data engineer builds and maintains the structure. They create the pipelines, organize the databases, and make sure the data gets from the source system to the place where it will be used. Think of them as the one who builds the plumbing the water flows through.

The data analyst uses that structure to answer questions about the present and the past. What happened, why it happened, how much we sold, where we are losing. They explain reality with data and help the business decide now.

The data scientist goes beyond explanation and works with prediction. They use statistics and machine learning models to estimate what may happen, forecast demand, calculate risk, recommend products. It requires more math and programming.

In most companies, the data analyst is the entry point into the field. It is the role with the most openings, the one that asks for the fewest math prerequisites, and the one that talks most to the business. That is why it is the best place to start.

Who works with data inside the company

The data analyst is rarely isolated. They serve areas that need a fast, reliable answer. Purchasing wants to know which suppliers the company spends the most with. Supply chain needs to understand stockouts and idle inventory. Marketing wants to know which campaign brought in better customers. Finance needs to close margin by product. HR tracks turnover. Each of these areas generates a question, and the analyst translates the question into data.

This is where an uncomfortable truth comes in. If each area understands "customer" in its own way, if material is duplicated, if the supplier has no standard classification, the analyst will spend the whole day reconciling databases instead of generating value. Anyone who has taken part in a project knows that the report that does not reconcile is almost never the tool's fault. It is the lack of reliable master data underneath.

Which tools the data analyst uses

The good news is that the core tools are accessible and you can learn without spending almost anything. The most important of all is SQL, the language for querying databases. There is no data analyst without SQL. It is the non-negotiable basic.

Next comes data visualization. The most requested in the Brazilian market are Power BI, Tableau, and Looker Studio. Power BI dominates job openings because of Microsoft's strength in companies. Advanced Excel is still widely used and should not be dismissed, because a lot of real analysis is still born in a spreadsheet.

For those who want to go further, programming comes in, usually Python with libraries such as Pandas, or R. Python has become an important differentiator because it lets you automate data processing and do analyses a spreadsheet cannot handle. It is not mandatory to start, but it opens doors fast.

There is also a layer that separates the ordinary analyst from the mature analyst, which is understanding data quality and governance tools. Catalog platforms such as Collibra, Alation, and Microsoft Purview, and quality tools such as Ataccama and Informatica, exist because reliable analysis depends on reliable data. An analyst who understands where the data comes from and whether it can be trusted is worth much more than one who only builds pretty charts.

How much a data analyst earns

Pay grows fast with experience, and the range varies quite a bit by region, company size, and sector. Using market references in Brazil for 2026, monthly values are around:

A junior analyst, with up to two years, usually earns between R$ 3,000 and R$ 5,000. A mid-level analyst, with two to five years, falls in the range of R$ 5,500 to R$ 8,000, and can exceed R$ 9,000 at large companies. A senior analyst, with more than five years, goes from R$ 9,000 to R$ 12,000, reaching R$ 15,000 or more at fintechs, consultancies, and international openings.

Two factors push that salary up strongly. The first is English, which opens remote openings abroad with pay in another currency. The second, and this is where I want to call attention, is understanding governance and master data. The professional who knows how to ensure the data is reliable, and not just analyze it, enters a range most do not reach.

How to become a data analyst

There is no single path, and the field is one of the most open to those coming from another background. I have seen people from business administration, accounting, engineering, and even purchasing move into data successfully. What counts is the set of skills, not the specific degree.

The practical path I recommend starts with SQL, which is the foundation. Next, a visualization tool, preferably Power BI, so you can deliver visible results. Then, fundamentals of analysis and basic statistics, so you do not just show numbers but interpret them. Only then, if you want to advance, Python.

But the step almost every course forgets is the most important for those who want to truly stand out. Understanding data quality and governance. There is no point mastering the tool if you do not know how to recognize when the data is wrong. The analyst who asks where this data came from, who owns it, and whether it can be trusted is the one who earns leadership's trust.

On certifications, they help but do not replace a portfolio. Power BI certifications and cloud data fundamentals are well regarded. For those who want to become a reference in data management, the CDMP certification, from DAMA, based on the DAMA-DMBOK, is the most respected and shows you understand governance, not just tools. More than any certificate, build a portfolio with real projects. Take a public dataset, do an end-to-end analysis, and show your reasoning.

Career opportunities

Demand for data analysts remains high in Brazil and worldwide, and shows no sign of cooling. Every company that digitized its processes started generating data, and data without someone to analyze it is a cost, not an asset. This holds for banking, retail, manufacturing, healthcare, logistics, agribusiness, and the public sector.

The career also has good growth ramps. From analyst, the natural paths are becoming a senior analyst, moving to data scientist, going on to data engineering, or taking the governance track, becoming a data steward, a data quality specialist, and, later, leadership positions such as data coordinator and manager. This governance track, in fact, is the one fewest people see and the one most in need of good professionals. It is where 4MDG operates every day, and where I see a real shortage of qualified people.

Main questions about the profession

Do you need a degree to be a data analyst? A specific degree is not mandatory. Higher education helps, but the market values proven skill and a portfolio. Many people get in through bootcamps, hands-on courses, and their own projects.

Do I need to know how to code? To start, no. SQL and a visualization tool already put you in the game. Python is a differentiator and helps you grow, but it is not an entry barrier.

Is a data analyst the same as a data scientist? No. The analyst explains the present and the past to support decisions. The scientist works with prediction and statistical models. The analyst is usually the entry point into the field.

How long does it take to become a data analyst? With focused study, it is possible to be ready for junior openings in something between six months and a year. What speeds it up is practicing on real projects, not accumulating courses.

What is the difference between an ordinary analyst and a sought-after analyst? The sought-after analyst does not just build the chart. They know whether the data can be trusted. Understanding data quality and governance is what separates those who deliver a report from those who deliver a safe decision.

MDM Academy: where to train the data professional the market is looking for

MDM Academy is 4MDG's school of governance and master data. It was born from a perception I live in practice every day. The market trains many people who know how to operate tools, but few people who understand the data underneath them. And it is that second person that companies need the most and find the least.

At the Academy, the focus is training professionals who understand data governance, master data, quality, master record maintenance, and the impact of all this on the business. It is content built by people who live real projects, with examples from purchasing, supply chain, SAP, and AI, translating the technical into results. If you want to enter the data field with a differentiator most people do not have, or you are already an analyst and want to move up a level, it is worth getting to know MDM Academy and joining the Data Club, our community of people who take data seriously.

Conclusion

Being a data analyst is one of the best entry points into the data field in Brazil today. Good demand, pay that grows fast, an accessible study path, and room to grow in several directions. But the message I always repeat remains. The tool you learn. What sets a professional apart is understanding that good analysis depends on reliable data, and reliable data depends on governance.

The analyst who sees this stops being someone who makes charts and becomes someone the company trusts to decide. And, in the end, that is what the market is hungry for. Not more reports, but people who guarantee that the number behind them is true.