Data Engineering for Beginners – Part 1.2 – Data Careers Explained: Engineer, Analyst, Scientist, BI Developer, or Analytics Engineer?

Data Engineering for Beginners Learning Path

Welcome to the Series

If you’re new to data engineering, it’s tempting to jump straight into learning Python or SQL. In fact, that’s exactly what most beginners do.

The problem is that without understanding how the data world fits together, the tools quickly become confusing.

This Data Engineering for Beginners series is designed differently.

Instead of learning isolated technologies, you’ll first learn why they exist and where they fit before building a complete end-to-end data engineering project from scratch.

Learning Path

Previously

In the previous article, we looked at how the data world works.
You learned that data usually moves through a lifecycle:

Source Systems

↓

Ingestion

↓

Storage

↓

Transformation

↓

Modeling

↓

Serving

↓

Consumption

That lifecycle explains why data platforms exist. But it also raises another important question: Who works on each part of that lifecycle? If you are new to data, the job titles can be confusing.

• Data Engineer.
• Data Analyst.
• Analytics Engineer.
• BI Developer.
• Data Scientist

Sometimes the titles sound similar. Sometimes companies use them differently. Sometimes two people with the same job title do completely different work depending on the organisation.

This article cuts through that confusion.

By the end, you will understand:

• What the main data roles are
• What each role actually does
• How the roles work together
• Which skills matter for each path
• Where each role fits in the data lifecycle
• How to start thinking about which direction suits you

This article is not about choosing one path yet. That comes in the next article. For now, the goal is simple:

Understand the landscape.

The Big Picture

Most data work exists to move information from raw source systems to useful business decisions. Different roles focus on different parts of that journey.

A simplified version looks like this:

Source Systems

↓

Data Engineer – Builds the pipelines and platform

↓

Analytics Engineer – Transforms and models trusted data

↓

BI Developer – Builds semantic models and dashboards

↓

Data Analyst – Answers business questions

↓

Business Decision

Data Scientists may also use the same data to build models, forecasts, predictions, or experiments. They build predictive and statistical models.

Anyway, these roles overlap. In a small company, one person might do several of them. In a large company, each role may be a separate team. The important thing is not memorising titles. The important thing is understanding the type of work each role is responsible for.

The Five Core Data Roles

In this article, we will focus on five common roles:

1. Data Engineer
2. Analytics Engineer
3. BI Developer
4. Data Analyst
5. Data Scientist

There are other roles too, such as Data Architect, Machine Learning Engineer, Database Administrator, and Analytics Manager.

But if you understand these five, you will understand most of the modern data career landscape.

1. Data Engineer

A Data Engineer builds the systems that move, store, and prepare data.

If data is created in one system and needs to be available somewhere else, a Data Engineer is often involved. Data Engineers work on the foundation of the data platform.

They make sure data is:

• Collected
• Stored
• Processed
• Reliable
• Available
• Scalable

A simple way to describe the role:

Data Engineers build the infrastructure that makes data usable for the other roles.

What Data Engineers Actually Do

A Data Engineer might:

• Extract data from APIs
• Load files into a data lake
• Build pipelines from source systems
• Write SQL transformations
• Create tables in a data warehouse
• Schedule jobs
• Monitor pipeline failures
• Improve performance
• Handle data quality issues
• Work with analysts to understand data needs

A typical task might be:

“We need customer, order, and payment data available every morning in the warehouse so analysts can build reports.”

The Data Engineer would design and build the process that makes that happen.

Where Data Engineers Fit in the Lifecycle

Data Engineers usually work here:

Source Systems

↓

Ingestion ← Data Engineer

↓

Storage ← Data Engineer

↓

Transformation ← Data Engineer / Analytics Engineer

↓

Modeling

↓

Serving

↓

Consumption

Their work often happens before most business users ever see the data. If the pipelines fail, dashboards break. If the data is incomplete, analysis becomes unreliable.

Data Engineering is often invisible when it works and very visible when it does not. This can sometimes be difficult, as you might not get much recognition when you are doing a great job.

Common Tools for Data Engineers

Data Engineers often use:

• SQL
• Python
• Git
• Cloud platforms
• Data warehouses
• Data lakes
• dbt
• Airflow
• Spark
• Docker

You do not need to know all of these as a beginner. But SQL, Python, and Git are usually the best starting points.

Who Might Enjoy Data Engineering?

You may enjoy Data Engineering if you like:

• Building systems
• Automating repetitive work
• Debugging problems
• Thinking in pipelines
• Working with databases
• Writing code
• Making things reliable
• Understanding how data flows

You may enjoy it less if you mainly want to:

• Present insights to stakeholders
• Spend most of your time in dashboards
• Avoid technical troubleshooting
• Focus primarily on business strategy

Data Engineering is a technical role. It is also highly practical. The work is about making data available, trustworthy, and usable.

2. Analytics Engineer

Analytics Engineering is a newer role that sits between Data Engineering and Data Analysis. An Analytics Engineer usually works after raw data has been loaded into a warehouse. Their job is to turn raw or semi-clean data into trusted, business-friendly models.

A simple way to describe the role:

Analytics Engineers turn raw warehouse data into clean analytical datasets.

What Analytics Engineers Actually Do

An Analytics Engineer might:

• Write SQL models
• Build fact and dimension tables
• Define business metrics
• Create reusable transformations
• Test data quality
• Document datasets
• Work with analysts to standardise definitions
• Use dbt to manage transformation logic

A typical task might be:

“Different reports calculate revenue differently. We need one trusted revenue model that everyone can use.”

The Analytics Engineer would create a clean model where revenue is defined consistently.

Where Analytics Engineers Fit in the Lifecycle

Analytics Engineers usually work here:

Source Systems

↓

Ingestion

↓

Storage

↓

Transformation ← Analytics Engineer

↓

Modeling ← Analytics Engineer

↓

Serving ← Analytics Engineer / BI Developer

↓

Consumption

They are especially important in companies using a modern data stack. Data Engineers may focus on getting data into the warehouse. Analytics Engineers focus on making that data usable for analysis.

Common Tools for Analytics Engineers

Analytics Engineers often use:

• SQL
• dbt
• Git
• Data warehouses
• Data quality tests
• Documentation tools
• BI tools
• Sometimes Python

The role is usually more SQL-heavy than Python-heavy.

If you enjoy structured thinking, clean SQL, and business logic, Analytics Engineering can be a very good fit.

Who Might Enjoy Analytics Engineering?

You may enjoy Analytics Engineering if you like:

• SQL
• Data modelling
• Clean structures
• Business definitions
• Documentation
• Testing data
• Working close to analysts
• Making numbers consistent

You may enjoy it less if you want to focus mostly on:

• Infrastructure
• APIs
• Cloud networking
• Low-level engineering
• Machine learning

Analytics Engineering is a strong option for people who enjoy both technical work and business context.

3. BI Developer

A BI Developer builds reporting and analytical solutions. BI stands for Business Intelligence. BI Developers often work close to the serving and consumption layers of the data lifecycle. Their job is to make data understandable and useful for business users.

A simple way to describe the role:

BI Developers turn data models into dashboards, semantic models, and reports.

What BI Developers Actually Do

A BI Developer might:

• Build Power BI dashboards
• Create semantic models
• Write DAX measures
• Define KPIs
• Optimise report performance
• Manage relationships between tables
• Design report navigation
• Work with stakeholders
• Maintain reporting solutions
• Validate figures with business users

A typical task might be:

“The sales team needs a dashboard showing revenue, margin, customers, and product performance.”

The BI Developer would build the model and report that make those numbers accessible.

Where BI Developers Fit in the Lifecycle

BI Developers usually work here:

Source Systems

↓

Ingestion

↓

Storage

↓

Transformation

↓

Modeling ← BI Developer / Analytics Engineer

↓

Serving ← BI Developer

↓

Consumption ← BI Developer / Analyst

In some organisations, BI Developers also do data modelling in the warehouse. In others, they mainly work inside tools like Power BI, Tableau, or Looker.

Common Tools for BI Developers

BI Developers often use:

• Power BI
• DAX
• SQL
• Power Query
• Tabular models
• Excel
• Tableau
• Looker
• Semantic layers

In Microsoft-focused organisations, Power BI and DAX are especially important.

Who Might Enjoy BI Development?

You may enjoy BI Development if you like:

• Building dashboards
• Creating useful reports
• Designing data models
• Writing business calculations
• Working with stakeholders
• Turning data into visual stories
• Making complex information easy to understand

You may enjoy it less if you want to focus mostly on:

• Backend pipelines
• Infrastructure
• Advanced software engineering
• Machine learning research

BI Development is a great path if you enjoy both technical modelling and business-facing work.

4. Data Analyst

A Data Analyst uses data to answer business questions. Analysts are usually closer to stakeholders than Data Engineers are. They help the business understand what happened, why it happened, and what action might be needed.

A simple way to describe the role:

Data Analysts turn data into answers.

What Data Analysts Actually Do

A Data Analyst might:

• Write SQL queries
• Build dashboards
• Analyse trends
• Investigate business problems
• Prepare reports
• Present findings
• Work with Excel
• Validate business numbers
• Explain insights to stakeholders
• Recommend actions

A typical task might be:

“Revenue dropped last month. Can you find out why?”

The analyst would examine the data, identify possible causes, and communicate the findings.

Where Data Analysts Fit in the Lifecycle

Data Analysts usually work here:

Source Systems

↓

Ingestion

↓

Storage

↓

Transformation

↓

Modeling

↓

Serving

↓

Consumption ← Data Analyst

Analysts mostly consume prepared data.

But in many companies, analysts also clean data, write SQL transformations, build dashboards, and define metrics.

This is why Data Analyst roles can vary a lot.

Common Tools for Data Analysts

Data Analysts often use:

• SQL
• Excel
• Power BI
• Tableau
• Looker
• Python, sometimes
• Statistics, sometimes

While SQL is the most important technical skill for most analysts, communication is just as important.

An analyst who finds the right answer but cannot explain it clearly will struggle to create impact.

Who Might Enjoy Data Analysis?

You may enjoy Data Analysis if you like:

• Solving business problems
• Asking questions
• Finding patterns
• Explaining results
• Building dashboards
• Working with stakeholders
• Turning numbers into recommendations

You may enjoy it less if you prefer:

• Deep infrastructure work
• Heavy programming
• Backend systems
• Working far from business users

Data Analysis is often the most accessible entry point into data.

It is also a strong foundation for moving later into BI, Analytics Engineering, or Data Engineering.

5. Data Scientist

A Data Scientist uses statistics, experimentation, and machine learning to solve problems with data. Where analysts often focus on understanding what happened, data scientists often focus on predicting what might happen next.

A simple way to describe the role:

Data Scientists use data to build models, predictions, and experiments.

What Data Scientists Actually Do

A Data Scientist might:

• Build predictive models
• Analyse customer behaviour
• Create forecasting models
• Run experiments
• Evaluate model performance
• Prepare training datasets
• Use machine learning algorithms
• Work with notebooks
• Collaborate with engineers to deploy models
• Communicate model results to stakeholders

A typical task might be:

“Can we predict which customers are likely to churn?”

The Data Scientist would explore historical customer data, build a model, evaluate accuracy, and help turn the result into a business process.

Where Data Scientists Fit in the Lifecycle

Data Scientists usually work here:

Source Systems

↓

Ingestion

↓

Storage

↓

Transformation

↓

Modeling

↓

Serving

↓

Consumption / ML Use Cases ← Data Scientist

Data Scientists depend heavily on good data engineering.

Without clean, reliable data, machine learning models are unreliable.

This is one reason Data Engineering has become even more important as AI adoption grows.

Common Tools for Data Scientists

Data Scientists often use:

• Python
• SQL
• Jupyter notebooks
• pandas
• scikit-learn
• statistics
• machine learning libraries
• visualization libraries
• cloud ML platforms

Data Science usually requires stronger statistics and mathematics than the other roles.

It can also be more competitive at junior level because many people are attracted to the title.

Who Might Enjoy Data Science?

You may enjoy Data Science if you like:

• Statistics
• Machine learning
• Experiments
• Research-style work
• Python
• Modelling uncertainty
• Predictive problems
• Evaluating model performance

You may enjoy it less if you mainly want:

• Clear production systems
• Business dashboards
• SQL modelling
• Infrastructure work
• Lower mathematical complexity

Data Science can be very rewarding, but it is not always the best first role for beginners.

Many people are better served by starting in analysis, BI, analytics engineering, or data engineering first.

How the Roles Work Together

Let’s return to the online shop example from the previous article.

A customer buys a bicycle. That transaction creates data in the webshop database. Different roles may interact with that data at different stages.

Customer Purchase

↓

Application Database

↓

Data Engineer

Builds pipeline into warehouse

↓

Analytics Engineer

Creates clean sales model

↓

BI Developer

Builds Power BI semantic model and dashboard

↓

Data Analyst

Investigates sales trends

↓

Business Team – Makes decisions

A Data Scientist might also use the same data to build:

• A churn prediction model
• A demand forecast
• A recommendation engine
• A customer segmentation model

The same source data can support many different use cases.

That is why good data foundations matter.

A Practical Comparison

Here is a simple comparison of the roles.

Another way to think about it:

Data Engineer:

Can we get the data reliably?

Analytics Engineer:

Can we make the data clean and consistent?

BI Developer:

Can we make the data easy to explore?

Data Analyst:

What does the data tell us?

Data Scientist:

Can the data help us predict or optimise something?
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Skills Comparison

Different roles require different skill combinations.

Data Engineer

Most important skills:

• SQL
• Python
• Data modelling
• Cloud basics
• Pipelines
• Git
• Debugging
• System design fundamentals

Analytics Engineer

Most important skills:

• SQL
• dbt
• Git
• Data modelling
• Data testing
• Documentation
• Business logic
• Metric definitions

BI Developer

Most important skills:

• Power BI or similar BI tool
• SQL
• DAX or semantic modelling language
• Data modelling
• Dashboard design
• Performance optimisation
• Stakeholder communication

Data Analyst

Most important skills:

• SQL
• Excel
• Power BI, Tableau, or Looker
• Business understanding
• Communication
• Basic statistics
• Data storytelling

Data Scientist

Most important skills:

• Python
• SQL
• Statistics
• Machine learning
• Experimentation
• Data preparation
• Model evaluation
• Communication

The One Skill Everyone Needs

If you are starting from zero, learn SQL first. SQL appears in almost every data role.

• Data Analysts use SQL to query data.
• BI Developers use SQL to prepare and validate reporting datasets.
• Analytics Engineers use SQL to build models.
• Data Engineers use SQL to transform and test data.
• Data Scientists use SQL to extract training datasets.

No matter which path you choose later, SQL will help. Python is also very valuable, especially for Data Engineering and Data Science. But SQL is the most universal starting point.

The Role Boundaries Are Not Always Clean

In real companies, job titles are messy.

• A Data Analyst may build Power BI dashboards.
• A BI Developer may write SQL transformations.
• An Analytics Engineer may do data engineering work.
• A Data Engineer may build dimensional models.
• A Data Scientist may spend most of the week cleaning data.

This does not mean the titles are useless.

It means you should pay attention to the actual responsibilities, not only the job title.

When reading job descriptions, look for the work being described:

• Are they asking for dashboards?
• Are they asking for pipelines?
• Are they asking for SQL models?
• Are they asking for machine learning?
• Are they asking for stakeholder presentations?
• Are they asking for cloud infrastructure?

The responsibilities tell you more than the title.

Which Role Is Best for Beginners?

There is no single answer. It depends on your background and interests. But here is a practical starting point:

If you enjoy business questions start with:

Data Analyst

This is often the most accessible entry point.

If you enjoy dashboards and data models Start with:

BI Developer

This is a strong path if you enjoy Power BI, reporting, and business logic.

If you enjoy SQL and clean modelling consider:

Analytics Engineer

This is a good fit if you like structured transformation work.

If you enjoy coding and systems

Consider:

Data Engineer

This is a strong technical path with good long-term demand.

If you enjoy statistics and machine learning

Consider:

Data Scientist

This path can be rewarding, but it often requires stronger mathematical preparation.

How This Connects to the Hands-On Series

This series is focused mainly on Data Engineering and Analytics Engineering. But it will also touch BI Development, because the final output will be a Power BI dashboard.

The hands-on project later in the series will follow this flow:

Raw Data

↓

Python Ingestion

↓

Staging Area

↓

SQL Transformations

↓

Dimensional Model

↓

Power BI Dashboard

That means you will practise skills from several roles:

Data Engineering Skills

• Ingesting data
• Structuring files
• Building repeatable processes
• Thinking in pipelines

Analytics Engineering Skills

• Cleaning data
• Writing SQL transformations
• Creating fact and dimension tables
• Applying business logic

BI Development Skills

• Building a Power BI model
• Creating DAX measures
• Designing useful reports

This is intentional. A good beginner project should show how the roles connect. Even if you later specialise, understanding the full flow makes you better at your job.

Common Beginner Misunderstandings

Misunderstanding 1: Data Scientist Is the “Highest” Data Role

Many beginners assume Data Scientist is the most advanced or best data role. That is not true. Data Scientist is simply a different role. A senior Data Engineer, Analytics Engineer, or BI Developer can be just as valuable and well-paid.

Choose based on the type of work you enjoy, not the title that sounds most impressive.

Misunderstanding 2: Data Analysts Do Not Need Technical Skills

Data Analysts need technical skills.

SQL is especially important.

Many analysts also use Power BI, Excel, statistics, and sometimes Python.

The difference is that analysts usually apply technical skills to business questions rather than infrastructure problems.

Misunderstanding 3: Data Engineers Only Move Data

Moving data is part of the job.

But good Data Engineers also think about:

• Reliability
• Testing
• Performance
• Data quality
• Scalability
• Cost
• Maintainability
• Security

The role is not just about copying data from A to B.

It is about building trustworthy data systems.

Misunderstanding 4: BI Is Just Making Pretty Charts

Good BI work is much deeper than visual design.

BI Developers often work with:

• Data models
• Business definitions
• DAX measures
• Performance optimisation
• Security
• User experience
• Stakeholder expectations

A beautiful dashboard with incorrect numbers is useless.

Misunderstanding 5: Job Titles Are Consistent Everywhere

They are not. The same title can mean different things at different companies. Always read the responsibilities. The job description matters more than the job title.

Key Takeaways

If you are new to data careers, remember these ideas:

• Data Engineers build the pipelines and platforms that make data available.
• Analytics Engineers transform raw data into clean, trusted models.
• BI Developers build semantic models, reports, and dashboards.
• Data Analysts use data to answer business questions.
• Data Scientists use data for models, predictions, and experiments.
• SQL is the most useful starting skill across almost all data roles.
• Job titles vary, so focus on responsibilities rather than titles.
• The hands-on project in this series will combine Data Engineering, Analytics Engineering, and BI Development skills.

What Comes Next

Now that you understand the main data roles, the next article narrows the focus.

We will look specifically at Data Engineering.

You will learn:

• What Data Engineers actually do
• What a normal day can look like
• What skills you need
• What kind of person enjoys the work
• What the downsides are
• Whether this path is right for you

The purpose of the next article is to help you decide whether you want to continue into the hands-on Data Engineering path that begins later in the series.

First, understand the world. Then, understand the roles. Next, decide whether Data Engineering is your path.

Next up → 1.3: Who Does What? Roles & Teams in Data — a deeper look at every data role, how teams are structured, and how to pick the right path for you.