Career path to becoming a Data Engineer

Informatec Digital » Resources » Career path to becoming a Data Engineer

The career path to becoming a Data Engineer It has become one of the most attractive fields in the world of data, especially for those with backgrounds such as Data Analyst or a Data Scientist And they're looking to take a more technical approach. More and more companies need people capable of designing, building, and maintaining the systems that move information, not just machine learning models or dashboards.

At the same time, the amount of resources, courses and recommendations The information circulating online can be overwhelming: whether to start with Python, or begin with SQL and visualization, or go straight to the cloud or Spark… In this article, you will find a complete learning path in Spanish, based on reference content and expanded with practical context, so you know exactly where to start, how to progress, and what decisions to make in your development as a Data Engineer.

What is a Data Engineer and why is their role booming?

Un The Data Engineer is responsible for designing, building, and launching The systems that collect, transform, store, and make available the data that companies use to make decisions. While a data scientist focuses more on models and analysis, a data engineer ensures that the information is delivered on time, reliably, scalably, and securely.

In practice, the daily work of a Data Engineer It usually includes building ETL or ELT pipelines, process orchestration, and designing data architectures (data lakes, data warehouses, datamarts), the integration of multiple sources and collaboration with other teams such as analytics, data science or product.

According to various industry reports, The demand for Data Engineers continues to grow And their salaries are generally higher than those of data science profiles in many markets, precisely because of the direct impact they have on the technical infrastructure and the company's ability to leverage its data.

Platforms specializing in data training highlight that over 70% of Data Engineer job postings They require solid knowledge of software engineering and Distributed systemsand that salary ranges for this role can easily exceed those of other more analytical profiles when programming, cloud, and architecture skills are combined.

From Data Scientist to Data Engineer: why many make the transition

In many organizations, especially startups or growing companies, the boundaries between Data Scientist and Data Engineer They are not at all clear. Typically, the person training the models also has to clean data, build extraction scripts, move files, automate processes, and even set up APIs to serve predictions.

If you've ever found yourself building pipelines, deploying models "by hand" or connecting a thousand data sourcesChances are you're already working very closely with what a Data Engineer does. This technical exposure often sparks an interest in mastering the entire workflow, from data ingestion to production, and not relying so heavily on other teams or makeshift solutions.

A key reason for this change is the technical autonomyWhen you understand how data platforms are designed, what technologies are behind them, and how they are deployed in the cloud, you can bring your ideas to production more robustly, without getting stuck on experimental notebooks that never reach the end user.

Furthermore, The job market is strongly seeking data engineering profilesWhile purely data science roles tend to stabilize, the need for people to build data infrastructure, real-time pipelines, and scalable systems is growing, making the transition a rather strategic decision for the coming years.

Professional route levels: beginner, intermediate, and advanced

To avoid getting overwhelmed with so much information, it's useful divide the Data Engineer path into three levels maturity levels: beginner, intermediate, and advanced. The idea is not to pigeonhole you, but to help you prioritize what to learn first based on your starting point.

On the level beginner The fundamentals are grouped together: programming, logic, version control, and basic databases. This is what you need if you're starting practically from scratch or coming from a less technical background, such as a more business-oriented or analyst role.

On the level intermediate Topics covered include Big Data, distributed processing tools, ETL pipeline design, and orchestrators. Here you'll begin to explore technologies you'll see in production environments and start thinking like a data architect.

On the level advanced Cloud capabilities, certifications, security, continuous deployment, real-time streaming, and the cloud itself are all included. job search and technical interview preparationThis is the phase in which you aim for more senior or specialized positions.

As a rule of thumb, if You're not programming fluently yet.It makes more sense to start with the Programming and Databases section. If you're already comfortable with SQL and some Python, you can jump more quickly to Big Data and Data Processing. And if your goal is a cloud certification, the Cloud section will be key.

Programming fundamentals and version control

The foundation of almost everything in data engineering is knowing how to program with sound judgmentIt's not just about writing scripts that "work," but about creating maintainable, readable, and easy-to-debug code. In this area, Python is often the best entry point due to its simple syntax and its enormous ecosystem in data science and data engineering.

At this stage it's advisable to push hard the fundamental concepts of programmingData types, structures (lists, dictionaries, sets), functions, classes, error handling, and file reading and writing are all covered. If you prefer other languages ​​like Java, Scala, R, or even Julia, those are also valid, but in the real world of data engineering, Python and Java/Scala are the best.

In parallel, it is essential to learn version control with GitMany see it only as useful for teamwork, but it actually allows you to track your code's history, understand what changed and when, test ideas without fear, and keep your work organized. GitHub or GitLab will become your everyday platforms for hosting repositories and collaborating.

You don't need to become a Git guru from day one, but you do master the basic commands (init, add, commit, branch, merge, push, pull) and understanding how branches, pull requests, and code reviews work. This way of working is the norm in any minimally serious technical team.

Databases, SQL, and information modeling

Once the programming foundations are established, it's time to delve into databases and SQLThis is where many people get confused about the order: Python first, then SQL, or vice versa? The most sensible approach is to progress in parallel, but ensuring that handling SQL becomes second nature to you.

For structured data, a highly recommended option is Getting started with PostgreSQLBecause of its power and because it's the de facto standard in many projects. If you're already familiar with MySQL, SQLite, or other engines, it will still work, although PostgreSQL tends to offer more flexibility in professional environments.

It is also a good idea to become familiar with NoSQL databasessuch as MongoDB for documents or Redis for key-value pairs, as well as others like Cassandra for columns. The idea is not to memorize them all, but to understand their use cases, their advantages and disadvantages, and to know when to choose one over another.

This is where the data modelingRelational model, dimensional model, concepts of facts and dimensions, normalization, primary and foreign keys, referential integrity. You will learn to think in terms of table schemas, relationships, and efficient queries, which is crucial for any subsequent architecture.

Later on, you will delve deeper into data lakes, data warehouses, data marts and data hubsIn addition to approaches such as column versus row storage, star schema, snowflake schema, and on-read versus on-write schema strategies, this will give you the language and patterns used in real-world projects to organize information at scale.

Concepts of Big Data, analytics and business intelligence

With a clear understanding of SQL and database fundamentals, it's a good idea to take a look at the concepts of Big Data and analyticsYou don't need to become an expert in every framework in the ecosystem, but you do need to understand what problems they try to solve and why they exist.

The world of Big Data relies on distributed processingIn this model, instead of running everything on a single machine, the workload is distributed across many nodes. Tools like Apache Spark have become very popular for processing large volumes of data, both in batch and streaming, and are often part of the technology stacks of data-driven companies.

In addition to Big Data, it is interesting to gain an overview of artificial intelligence, machine learning and business intelligenceAlthough as a Data Engineer you won't have to train complex models, you will have to prepare the data for them and design infrastructures that feed them.

You'll also see how things like BI tools (Power BI, Tableau, Looker, etc.), reporting processes, and the needs of business analysts. Understanding their workflows will help you design more useful data pipelines and models for those who consume the information.

Data processing: ETL, orchestration and data pipelines

The true heart of data engineering is the design and construction of data pipelinesHere you will learn exactly what an ETL (Extract, Transform, Load) is, when an ELT approach makes sense, how to orchestrate tasks, monitor them, and recover from failures.

A typical pipeline includes phases of data ingestion from multiple sources (APIs, databases, files, message queues), cleaning and transformation steps (normalization, aggregations, enrichments) and finally loading into some target system, which can be a data warehouse, a data lake, a NoSQL database or a mix of several.

In this context, tools emerge to flow orchestration such as Apache Airflow or other modern alternatives, which allow you to define dependencies between tasks, schedule executions, keep track of what has been executed, and react to errors. Although each company uses a different stack, the mindset of orchestrating and automating processes is common to all.

A key point is the catalog of concepts that are usually used in these environments: relational and dimensional model, data lake, data mart, data warehouse, column or row design, star and snowflake schemasand reading and writing strategies with different schemes. A clear understanding of this terminology will allow you to understand technical documentation, specialized books, and architectural diagrams.

This section is one of those that benefits most from practical exercises and small personal projects, where you can build end-to-end pipelineseven if it's with public data, and practice the typical patterns that you will later see in professional roles.

Security in pipelines and data platforms

The first step is to apply the principle of least privilege in roles and permissionsEach service, user, or application account should have only the access strictly necessary to perform its job, and nothing more. This reduces the attack surface and limits the impact of errors or leaks.

It is also essential to understand how it works encryption of data in transit and at restUse HTTPS, TLS and secure protocols when moving data between services, and enable encryption on databases, storage buckets or other systems where information is stored.

When exposing APIs or model services, you must pay attention to details such as the authentication and authorization (tokens, API keys, OAuth, etc.), limit access to critical endpoints, and log system activity to audit for misuse. You don't need to be a security expert, but you do need a sufficient level of expertise to make responsible decisions.

All this not only prevents scares, but also Strengthen your professional profile in the eyes of the company, since you demonstrate awareness of the real impact of your work on the business and on the protection of customer and user data.

Types of storage and data architecture design

When transitioning from working with static datasets as a data scientist to becoming a data engineer, completely changes your relationship with storageIt's no longer about opening a CSV locally, but about designing systems that support continuous data flows, changing schemas, and multiple consumers simultaneously.

In your day-to-day life you will combine different types of storage: relational databases (PostgreSQL, MySQL) for structured and transactional information; NoSQL databases such as MongoDB (documents), Redis (key-value) or Cassandra (columns) for specific needs of performance, schema flexibility or horizontal scaling.

To this is added the cloud storage of objects (Amazon S3, Azure Data Lake Storage, Google Cloud Storage), which has become the cornerstone of many modern data lakes. Large volumes of raw and processed data are stored here, generally in formats like Parquet or Avro, ready to be consumed by various analytics engines.

Designing modern data architectures involves thinking about how data flows From the source to the consumer, what intermediate layers of quality, governance, or transformation are needed, and how can all of this be organized to make it maintainable? Knowing how to read and create architectural diagrams will be a regular part of your work.

Furthermore, many organizations are adopting streaming-centric architectures, in which technologies such as Apache Kafka They play a leading role as the backbone of events, which brings us to the next section.

Streaming and real-time processing with Apache Kafka

Much of traditional data analysis has been done in batch mode: periodically load data, process it, and generate results.However, more and more companies need to react in real time to what is happening, from financial transactions to user activity or IoT sensors.

In this context, Apache Kafka emerges as event streaming platform Adopted by tens of thousands of organizations worldwide, Kafka allows users to publish and consume messages in topics, with decoupled producers and consumers, and scale the system to handle from a few to millions of events per second.

For a Data Engineer, understanding well Kafka's architecture Key concepts include: what topics, partitions, brokers, producers, consumers, consumer groups, and offsets are. Also, how to integrate Kafka with downstream systems (databases, data warehouses, alert systems) and with real-time analytics processes.

Many machine learning models are also starting to run on data streams, which forces them to combine MLOps with streaming platforms to deliver live predictions. Kafka ceases to be "just another technology" and becomes the core of modern event-centric architectures.

IT managers at large companies consider streaming systems as key component of their data and AI strategiesReporting significant improvements in return on investment when adopting these architectures. Learning Kafka and related concepts puts you a step ahead of many candidates.

Containers, Docker, and service deployment

In the transition from data scientist to data engineer, a turning point is mastering Packaging and deploying services with DockerYou go from running scripts on your machine to building images that can be launched on any server or cloud environment without dependency surprises.

Docker allows you to define in a Dockerfile Everything you need to run your applicationPython or Java version, libraries, basic configurations… Then you just have to build the image, test it locally, and run the container wherever needed. This greatly reduces the classic “it works on my computer” scenario and facilitates collaboration with DevOps.

For a Data Engineer, it is common to package ingestion services, model APIs, processing workers or containerized orchestration tasks. These containers are then integrated into platforms like Kubernetes or other orchestrators, although that step may come later.

Reference publications and technical communities insist that Docker has become an almost indispensable skill For those who work with model deployment and pipelines, because it allows you to reproduce environments, automate deployments, and version infrastructures in a way similar to how you version code.

Production models: from script to API with Flask or FastAPI

Another essential block in this path, especially if you come from Data Science, is learning to Exposing models as web servicesIt is no longer enough to save a pickle or a configuration file: APIs must be created that other computers or applications can consume.

Lightweight frameworks such as Flask or FastAPI They are ideal for this. With them, you can set up an API in just a few lines that receives data via POST, runs your model, and returns the prediction in JSON format. These services can then be integrated into larger architectures or streaming flows.

Combining this capability with Docker allows you to create self-contained containers with your modelReady to be deployed on various platforms. Furthermore, FastAPI includes easy integration with OpenAPI schemas and Swagger-style automated documentation, making life easier for those who consume your service.

This approach is the gateway to the world of MLOpsThis involves not only deploying a model, but also monitoring its performance, versioning data, automating retraining, and managing the entire lifecycle in production. Even if your focus as a Data Engineer isn't exclusively on MLOps, understanding this context is important.

The difference between a model that remains permanently on a laptop and one that is on a robust and monitored endpoint is enormous in terms of value to the company, and Data engineering is right at the center of that transformation.

The cloud as the natural environment for the Data Engineer

Today, most data platforms are built on some public cloud providerEspecially AWS, Google Cloud, or Azure. To complete your career path, it's important to commit to learning at least one ecosystem in some depth.

An interesting first option is the combo Databricks + Apache SparkEspecially if you're already familiar with PySpark. Databricks offers a managed environment for distributed clusters, collaborative notebooks, and a host of tools focused on data engineering and machine learning. Mastering this combination opens many doors in companies with large volumes of data.

Another lighter option, useful for prototypes, is to combine MongoDB with tools like Streamlitwhere you can store semi-structured data in MongoDB and build very fast dashboards or data applications with Streamlit without much additional infrastructure.

If you want to go for a more “cloud-native” route, you can focus on AWS or GCP services such as Kinesis, Lambda, API Gateway, Pub/Sub, Dataflow, BigQuery, and similar tools, which allow you to build serverless workflows and scalable architectures almost from scratch. In many cases, large companies highly value real-world experience with these services.

Providers like Google Cloud offer Data Engineer-specific learning pathsWith collections of on-demand courses, hands-on labs, skill badges, and preparation for official certifications, this learning path allows you to structure your learning and track your progress until you're ready to take your exam.

Resources, repositories, and how to practice effectively

A very common question for those starting this route is which resources to choose and which projects to undertake So that learning doesn't remain purely theoretical. Nowadays, there are community repositories in Spanish with concepts, technical challenges, and collections of free materials that can serve as a living guide.

In these repositories, resources are usually marked by level (beginner, intermediate, advanced) And by language, to help you decide what to watch first. Although much content is in English, you can always use your browser's "translate to Spanish" option or take advantage of automatic subtitles and transcripts in videos.

Some examples of useful practices include challenges like “100 days of data engineering”where you commit to dedicating some time each day to building something: a small pipeline, a cleanup script, a data model, an API connector, and so on. Consistency usually pays off more than occasional bursts of activity.

It is also highly recommended to read books and design patterns geared towards data engineeringAlthough many are in English, they teach proven approaches to designing robust systems, expose you to real-world architectures, and help you avoid common beginner mistakes.

If you find something truly useful, consider contribute to those repositories with improvements, translations, new resources, or corrections. Participating in open projects not only helps you learn, but also enhances your public portfolio with potential employers.

Job search, interview preparation and frequently asked questions

In the final stages of the route, it's time to focus on How to present your profile in the marketThis includes polishing your CV, creating a portfolio of data projects, maintaining an active profile on professional platforms, and practicing technical interviews specific to Data Engineers.

Companies usually value it highly. practical experience and own projects where it's clear what problem you solved, what technical decisions you made, what technology you used, and what results you obtained. You don't need to have worked as a Data Engineer before; a good, well-documented personal project can make all the difference.

Regarding frequently asked questions, the same ones always appear: which technical skills to prioritizeWhether it's worth learning Spark or Pandas and SQL are enough, whether it's worth investing time in cloud certifications, how long it takes to make the transition, or why some say that Data Analyst "is outdated".

In terms of skills, the winning combination is usually solid programming, advanced SQL, data modeling fundamentalsExperience managing at least one cloud platform and a basic understanding of orchestration and streaming are essential. Spark becomes highly relevant when dealing with large volumes of data or in environments where it's already implemented.

Regarding timelines, the time needed to transition from data scientist or developer to Data Engineer varies, but with a constant and well-focused dedicationIn a few months, you could be ready to apply for junior or transitional positions. The important thing is to build a solid foundation, avoid jumping from course to course without finishing any, and focus on projects that demonstrate your skills.

This path to data engineering combines theoretical foundations, lots of practice, and a good dose of curiosityBut in return, it opens the doors to one of the most in-demand and best-positioned profiles in the technology sector, with the added satisfaction of understanding and controlling the entire journey that data takes within an organization.