How to hire data engineers to build a scalable data infrastructure

Companies are generating more and more information. Applications, CRM, ERP, e-commerce platforms, marketing tools, financial systems, connected devices, and Cloud services continuously produce data that can be used to better understand the business and make decisions. However, having large amounts of information does not mean having a data strategy.

Before creating dashboards, developing Machine Learning models, or implementing Artificial Intelligence solutions, there is a fundamental challenge: ensuring that data is available, structured, updated, and ready to be used. This is where one of the most important roles within modern data teams appears: the Data Engineer. These professionals build the infrastructure that allows for the collection, transformation, storage, and distribution of information across different systems.

But when does a company really need to hire Data Engineers? What skills should they have? And what is the difference between a Data Engineer, a Data Analyst, and a Data Scientist? In this guide, we analyze how to identify the right profile and what aspects a company should consider before expanding its Data team.

What does a Data Engineer do?

A Data Engineer is the professional responsible for designing, building, and maintaining the systems that allow for the collection, processing, transformation, and storage of data. Their goal is to ensure that information flows correctly from various sources to the systems where it will later be used.

They can work with data from:

• Applications.

• APIs.

• CRM.

• ERP.

• Databases.

• Marketing platforms.

• E-commerce.

• Financial systems.

• IoT devices.

• External services.

All that information can be found in different formats, systems, and locations. The Data Engineer creates the architecture necessary to convert that scattered data into information that is accessible and usable by the rest of the organization.

Why has data engineering become a strategic capability?

For years, many organizations focused their data strategies primarily on visualization and reporting. However, any analysis depends on something prior: the quality and availability of the data.

A dashboard cannot provide reliable information if the source data contains errors. A Machine Learning model will hardly produce good results if the datasets are incomplete. And a business AI solution loses value if it cannot correctly access the organization's knowledge.

That’s why data engineering serves as the layer that connects operational systems with Analytics, Business Intelligence, Machine Learning, and Artificial Intelligence. The greater the dependency on data within the business, the more important this infrastructure becomes.

When does a company need to hire Data Engineers?

There are several signs that indicate the organization needs to strengthen its data engineering capability.

Data is spread across too many systems

CRM, ERP, commercial platforms, internal applications, and external tools can store information independently. When teams need to manually combine that data for analysis, there is an architectural problem. A Data Engineer can automate the integration between the different sources.

Reports require too much manual work

If each report requires exporting files, combining spreadsheets, or running manual processes, the organization likely needs to build data pipelines. Automating these processes allows information to arrive updated and ready for analysis.

You are building a Data Warehouse or Data Lake

Centralizing information requires decisions related to architecture, storage, transformation, and data governance. Data Engineers play a fundamental role during this process.

You want to implement Machine Learning or Artificial Intelligence

Before developing advanced models, you need properly prepared data. That’s why many AI projects actually start with a data engineering problem.

The volume of information is growing

Solutions that work with thousands of records may stop functioning when there are millions. In these scenarios, specialized technologies in distributed processing and scalable data architectures emerge.

ETL vs ELT: fundamental concepts in data engineering

Two concepts frequently arise when we talk about data engineering: ETL and ELT.

ETL — Extract, Transform, Load

Data is extracted from the original sources, transformed, and then loaded into the destination system.

ELT — Extract, Load, Transform

Data is extracted, first loaded into the destination platform, and then transformed using the processing capacity of that infrastructure. The choice depends on factors such as data volume, Cloud architecture, tools used, and analytical needs. A Data Engineer must be able to design these processes efficiently and maintainably.

What is a Data Pipeline?

A Data Pipeline is the set of processes that allows for the movement of information from one or more sources to a specific destination.

For example:

CRM → Pipeline → Data Warehouse → Power BI

or:

Application → Kafka → Data Lake → Machine Learning

These pipelines can include processes of:

• Extraction.

• Validation.

• Cleaning.

• Transformation.

• Enrichment.

• Storage.

• Monitoring.

When the infrastructure is well designed, much of these processes work automatically.

Data Warehouse vs Data Lake

This is another important decision within a data strategy.

Data Warehouse: It is primarily designed to store structured information that is ready for analysis. It is usually used for reporting, Analytics, and Business Intelligence.

Data Lake: It allows for the storage of large amounts of structured and unstructured data in its original format or with minimal prior transformation. It can be particularly useful for Big Data, Machine Learning, and certain advanced Analytics projects.

Additionally, there are modern architectures that combine features of both models.

The choice should respond to the actual needs of the business and not simply to technological trends.

What technologies should a Data Engineer master?

There is no single data engineering stack. However, certain technologies frequently appear in modern projects.

SQL: It remains one of the fundamental skills. A Data Engineer needs to understand queries, modeling, optimization, and manipulation of large volumes of information.

Python: Python is widely used for processing, automation, and data integration.

Apache Spark: It allows for processing large amounts of information through distributed architectures.

Apache Kafka: It is especially used in systems that need to process data streams in real time.

Apache Airflow: It allows for the orchestration and automation of workflows and data pipelines.

Databricks: A platform used for data engineering, Analytics, and Machine Learning on modern architectures.

Snowflake: A widely used Data Cloud Platform for the storage, processing, and analysis of information. The right profile will always depend on the existing stack and the architecture the company wants to build.

Data engineering in AWS, Azure, and Google Cloud

The cloud has profoundly changed the way data platforms are built. Currently, AWS, Microsoft Azure, and Google Cloud offer specialized services for storage, processing, integration, and analysis. This means that a Data Engineer may need specific knowledge of the provider used by the organization. However, their experience with a particular tool should not be the only evaluation criterion.

A good specialist must understand architectural concepts that can be transferred across different technologies.

Data Engineer vs Data Analyst vs Data Scientist

This distinction is especially important before starting a hiring process.

The profiles are related but not interchangeable. A common mistake is hiring a Data Scientist expecting them to solve structural problems of data integration and architecture. If the data is not yet properly organized, the first profile the organization likely needs is a Data Engineer.

Junior, Senior, or Data Architect?

We must also determine the level of responsibility.

Junior Data Engineer: Can perform defined tasks on existing pipelines and architectures under technical supervision.

Mid-Level Data Engineer: Can build integrations, develop pipelines, and work with greater autonomy.

Senior Data Engineer: Suitable for complex architectures, large volumes of information, and more responsible technical decisions.

Data Architect: Defines the overall architecture, standards, technologies, and technical strategy of the data platform. The more important data is to the business, the more relevant it is to have profiles capable of making architectural decisions.

Should you hire internally or outsource Data Engineers?

Building a complete data team internally can take time and require different specializations. That’s why some organizations choose to expand their capacity through Outsourcing IT.

This model allows for the incorporation of one or more Data Engineers to strengthen an existing team, develop a new platform, or address a specific technological need. When the project requires multiple disciplines, Team as a Service allows for the formation of a complete team.

For example:

Data Architect + Data Engineers + Data Analyst + ML Engineer + Cloud Engineer.

The composition can evolve as the project progresses. This is particularly interesting because the needs of a data project often change between the phases of architecture, implementation, analysis, and exploitation.

How lateam incorporates data specialists

At lateam, we help companies in Europe and the United States incorporate Data Engineers and other data specialists through IT Outsourcing and Team as a Service models. Before selecting profiles, we analyze the information sources, existing architecture, data volume, Cloud platforms, tools, and project objectives.

From there, we can identify professionals with experience in technologies such as SQL, Python, Spark, Kafka, Airflow, Databricks, Snowflake, AWS, Azure, and Google Cloud. We also value experience working with international teams, Agile methodologies, and the ability to collaborate with Analytics, Business Intelligence, Machine Learning, and Artificial Intelligence areas. Because a data strategy does not begin with a tool.

It begins by building an infrastructure that allows for trust in the information.

Frequently asked questions about Data Engineers

What does a Data Engineer do?

They design, build, and maintain systems that allow for the collection, transformation, processing, and storage of data so that it can later be used in Analytics, Business Intelligence, Machine Learning, or Artificial Intelligence.

What technologies should a Data Engineer master?

It depends on the project, although SQL and Python are common. They may also work with Spark, Kafka, Airflow, Databricks, Snowflake, and data services from AWS, Azure, or Google Cloud.

What is the difference between a Data Engineer and a Data Scientist?

The Data Engineer builds the infrastructure that allows for reliable and accessible data. The Data Scientist uses that data to perform advanced analyses and develop models.

When should you hire a Data Engineer?

When data is dispersed across different systems, there are too many manual processes, you need to build a Data Warehouse or Data Lake, or you want to prepare the infrastructure for Analytics, ML, or AI projects.

What is the difference between ETL and ELT?

In ETL, data is transformed before loading it into the destination system. In ELT, it is first loaded and then transformed using the processing capacity of the platform.

Can Data Engineers be outsourced?

Yes. They can be incorporated individually through IT Outsourcing or be part of a multidisciplinary team under a Team as a Service model.

Conclusion

Companies do not gain value simply by accumulating more information. Value appears when that data can be found, processed, and used reliably. That’s why, before building advanced dashboards, predictive models, or Artificial Intelligence solutions, many organizations need to first resolve their data infrastructure.

Hiring Data Engineers allows for building that foundation.

Pipelines, integrations, Data Warehouses, Data Lakes, and Cloud architectures are part of a technological layer that can later feed nearly all data-driven initiatives. Therefore, the question should not only be what tool your company needs.

Build your data team with the right profiles

At lateam, we help companies incorporate Data Engineers and data specialists through flexible IT Outsourcing and Team as a Service models.

Whether you need to strengthen an existing team or build a new data platform, we can help you identify the right professionals for your architecture and goals.