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Data Engineers
 
in U.S. time zones and provide support with payroll, taxes, local compliance, and access to best-in-class benefits.

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Hire the Top 1% of Data Engineers

Oscar C.

Fullstack Developer
Mountain Timezone

Experience

6 years

AVAILABILITY

Full-time

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Oscar C.

Flávia V.

Fullstack Developer
Central Timezone

Experience

11 years

AVAILABILITY

Full-time

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Flávia V.

Andres F.

Fullstack Developer
Eastern Timezone + 1

Experience

9 years

AVAILABILITY

Full-time

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Andres F.

Pedro M.

Game Developer
Pacific Timezone

Experience

8 years

AVAILABILITY

Full-time

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Pedro M.

Juliana B.

Game Developer
Mountain Timezone

Experience

7 years

AVAILABILITY

Full-time

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Juliana B.

Vanessa F.

Back-end Developer
Mountain Timezone

Experience

8 years

AVAILABILITY

Full-time

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Vanessa F.

Ana R.

Back-end Developer
Central Timezone

Experience

10 years

AVAILABILITY

Full-time

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Ana R.

Martina L.

Data Developer
Central Timezone

Experience

8 years

AVAILABILITY

Full-time

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Martina L.

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Very well-vetted, high-quality candidates, ensure that I don't waste time interviewing unqualified people. They also make payroll a breeze and allow us to offer competitive benefits packages and provide hardware to our employees. They help find solutions that work for you rather than just making up the numbers and building a funnel.
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Access Revelo's talent pool of Data Engineers with technical expertise across Libraries, APIs, Platforms, Frameworks, and Databases

Libraries

Frameworks

Facebook API | Instagram API | YouTube API | Spotify API | Apple Music API | Google API | Jira REST API | GitHub API | SoundCloud API

APIs

Amazon Web Services (AWS) | Google Cloud Platform (GCP) | Linux | Docker | Heroku | Firebase | Digital Ocean | Oracle | Kubernetes | Dapr | Azure | AWS Lambda | Redux

Platforms

Databases

MongoDB | PostgreSQL | MySQL | Redis | SQLite | MariaDB | Microsoft SQL Server

Tips for Hiring Data Engineers

Running a business that heavily relies on tech requires leaders to juggle numerous parts simultaneously, from the usual business departments, such as finance, marketing, and product development, to more niche and industry-specific areas. Data engineering is one type of tech expertise that many business leaders find particularly important.

Being able to harness the full power of data has proven to be essential for business growth based on customer and client feedback, as well as input from stakeholders and business partners. Data engineering propels companies forward by providing the insights necessary to make informed decisions.

All this makes data engineers highly sought-after professionals, as they manage and optimize data systems in alignment with their companies' future goals. However, recruiters and human resource managers must first understand the definition of data engineering and how it’s applied before finding and hiring data engineers to join their teams.

What Is Data Engineering?

Data engineering is the meticulous orchestration of data flows that aims to ensure data integrity and usability throughout the organization. Data engineering has become the essential backbone of any data-centric organization, playing a pivotal role in enabling startups and established businesses to harness the power of data for strategic decision-making. 

Projects that rely heavily on data tend to be highly diverse and complex, typically including the need to design and create robust databases tailored to an organization's specific needs. Similarly, building data pipelines is another critical component where companies need data engineers to develop sophisticated systems to transport data seamlessly from its source to its destination, maintaining quality and consistency.

Implementing ETL (Extract, Transform, Load) processes is also fundamental in data engineering. These processes involve extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a final destination, such as a data warehouse.

Why Data Engineering?

Data Engineering has become a necessity in today's data-driven world. It's valuable in many business applications, such as predictive analysis, customer segmentation, and decision-making. By leveraging data engineering, businesses ensure their data is clean, reliable, and available for use when needed. This paves the way for data analytics and data science, which provides critical insights to drive business growth.

One unique advantage of data engineering is its ability to transform raw and often unstructured data into meaningful, actionable information. By doing so, it provides companies with a significant competitive edge. Effective data engineering enables organizations to identify trends, forecast future scenarios, optimize operations, and personalize customer experiences.

In an era where data flows through most business transactions, the strategic importance of data engineering cannot be overstated. It not only supports data-driven decision-making but also underpins the development of advanced technologies like artificial intelligence (AI) and machine learning (ML), further augmenting a company's ability to innovate and stay ahead in a rapidly evolving market.

Benefits of Data Engineering

The benefits of data engineering are vast and varied. By integrating data engineering techniques into your business practices, organizations typically gain several advantages:

  • Improve data quality: Data engineers focus on cleansing, validating, and structuring your data, ensuring it's accurate and reliable. This level of data integrity is crucial for making informed business decisions.
  • Enhance data security: Data engineers are instrumental in implementing robust security measures that help maintain one organization's reputation and customer trust.
  • Facilitate data accessibility: Through well-designed systems and architectures, data engineers make data readily available for analysis and decision-making. This accessibility allows for more efficient business operations and timely insights.
  • Streamline data integration: Data engineers excel at integrating data from diverse sources, creating a unified and coherent data environment. This integration is critical to comprehensive analytics and provides a holistic view of business performance and market trends.
  • Drive innovation and efficiency: Data engineering enables businesses to innovate and optimize their operations by automating data processes and employing advanced analytics. This efficiency leads to cost savings and opens up new opportunities for growth and development.

Incorporating data engineering into the business strategy transforms data into a powerful asset, driving growth and competitive advantage in today's data-driven world.

What Does a Data Engineer Do?

A data engineer's job begins with managing and planning the collection and storage of data. They must ensure that data from various sources is accurately captured and stored. This involves setting up and maintaining complex databases and data warehouses that are both robust and scalable.

Beyond collection and storage, data engineers are also responsible for building and maintaining data pipelines, which are essential for transporting data efficiently from its source to platforms where it is analyzed and utilized. The process of data transformation is also a key responsibility; data engineers must clean, validate, and structure raw data, turning it into a format suitable for analysis.

Another critical role for data engineers is maintaining data quality and integrity, as they establish and enforce protocols to ensure the data's accuracy and consistency. They're also typically charged with implementing security measures to protect data and ensuring compliance with data privacy laws and regulations.

More often than not, data engineers find themselves in a collaborative role, working closely with data scientists, analysts, and other technology teams. Their work supports data-driven decision-making and enables the creation of advanced data models and analytics.

Required Skills and Qualifications for a Data Engineer

A typical data engineering role demands a unique blend of soft, hard, and technical skills so these professionals are able to manage and manipulate large data sets. Strong problem-solving abilities should be at the core of the data engineers' skillset, enabling them to tackle complex data-related challenges. They should also have high attention to detail to ensure the accuracy and integrity of data. Similarly, excellent communication skills are essential for collaborating with cross-functional software engineering teams and conveying technical information clearly.

From a technical standpoint, top data engineers must be proficient in SQL (or NoSQL or MySQL), Python, Java, and other relevant programming languages. These skills are fundamental for developing and maintaining sophisticated data infrastructures in modern data-driven environments. A solid understanding of data modeling and data warehousing is also crucial for structuring and storing data effectively, such as extensive knowledge of ETL (Extract, Transform, Load) processes for data transformation and integration.

Additionally, familiarity with advanced concepts such as machine learning models and artificial intelligence often greatly enhance a data engineer's ability to contribute to more complex data analysis projects. Proficiency in data visualization, algorithms, and automation is also increasingly important.

Data engineers should have excellent command over tools, platforms, and dashboards such as AWS (Amazon Web Services) for cloud computing, Hadoop for large-scale data processing, Spark for big data analytics, and Kafka for real-time data processing. These technologies are at the forefront of data engineering and are integral to handling vast amounts of data efficiently and effectively.

Popular Data Engineering Related Languages and Libraries

Data engineers often work with various programming languages and libraries. Some of the most common ones include:

  • Python: A versatile language popular for data manipulation and analysis
  • SQL: An essential language for managing and querying relational databases
  • Java: An object-oriented programming language convenient for big data projects, especially when combined with Hadoop
  • Apache Airflow: A platform to programmatically author, schedule, and monitor workflows
  • Spark: A fast and general engine for large-scale data processing

These languages and libraries collectively empower data engineers to handle diverse data-related tasks, ensuring efficient and accurate data analysis and management. It's not unusual for data engineers to utilize a variety of programming languages, frameworks, and libraries to help with their complex responsibilities. For example, Python and its many free libraries remain the go-to for many data engineers when it comes to data manipulation and analytics.

Popular Data Engineering Programming Tools

Data engineers use a variety of tools to carry out their tasks. Some of the popular ones include:

  • AWS Redshift: A fully managed, petabyte-scale data warehouse service
  • Microsoft Azure: Offers a range of cloud services, including those for computing, analytics, storage, and networking
  • Google Cloud: Provides a series of modular cloud services, including computing, data storage, data analytics, and machine learning
  • Apache Kafka: A distributed streaming platform

How Does Data Engineering Fit Into Modern Full-Stack/App Development?

Data Engineering has become integral to modern front-end, back-end, and app development, bridging the gap between raw data and functional applications. In front-end development, data engineers are instrumental in designing and implementing sophisticated data models. These make the backbone of user interfaces, directly influencing user experience by ensuring that the data driving these interfaces is structured, insightful, and responsive to user interactions.

The role of data engineers is equally pivotal in back-end development. They are responsible for managing the underlying data infrastructure. This includes tasks such as securing the storage of data, optimizing its processing, and ensuring seamless data availability. The work of data engineers ensures that the back-end systems support the complex data needs of modern applications, from handling large volumes of data to providing real-time data access.

For software app development, data engineers construct efficient data pipelines, which power app functionalities, enabling apps to offer dynamic content, personalized user experiences, and interactive features. They also ensure that apps are able to handle the influx of data generated by users, maintaining performance and reliability.

How Much Does It Cost to Hire a Data Engineer?

The average annual salary for a mid-level data engineer based in the US ranges between $86,000 to $125,000. This is influenced primarily by factors such as experience and location, but also by the complexity of the role and length of tenure. In addition to the base pay, many North American employees expect bonuses, benefits, and stock and equity options once they reach a high level of experience.

At the same time, data engineers in Latin American countries, despite being equally skilled, earn between 30% and 50% less than their US counterparts. This is primarily due to the lower cost of living in Latin America compared to the US.

Talent platforms like Revelo help connect you with highly qualified Latin American developers and data engineers who have been pre-vetted for experience, expertise, and English language skills. We offer a cost-effective solution to employers without compromising quality.

Featured Tool: Tech Team Budget Calculator

Finding Data Engineers for Your Project

Finding the right data engineers for your team is a strenuous process. It demands a deep understanding of your project’s specific needs and a strategic approach to recruitment.

The initial step should involve a thorough assessment of your project’s objectives and the technical challenges it presents. This understanding is crucial in identifying the skills and experiences needed in a data engineer, ensuring they align with your project’s demands.

Crafting a clear and comprehensive job description is the next vital step. This description should enumerate the technical competencies required, such as proficiency in specific programming languages or experience with certain data processing tools. It should also highlight the nature of the data problems the engineer will be solving and reflect the complexity and scale of data operations involved in the project. 

Through online forums such as LinkedIn or Github, hiring teams may be able to establish connections with prolific data engineers. Outside of an online talent search, software development networking and industry events provide hiring teams with the opportunity to create relationships with talent in an in-person setting.

Lastly, a robust hiring process is imperative. Hiring managers should evaluate candidates’ technical knowledge through interviews and technical assessments and gauge their problem-solving capabilities. They should also consider how well the candidates align with the organization’s culture and values. Assessing cultural fit is essential, as it ensures the data engineer is able to collaborate effectively with existing teams and contribute positively to the project’s overall dynamics.

Why Hire Data Engineers With Revelo?

The importance of having qualified data engineers for your business can't be overstated. They aid in everything from helping you leverage your data effectively to driving informed business decisions and facilitating long-term growth. However, acquiring specialized talent isn't only a complex process — it's also an expensive one.

Revelo simplifies the process of sourcing and hiring data engineers based in Latin America. With an extensive vetting process, we help you reach high-quality candidates who fit your business's needs. Plus, Revelo assists in handling payroll, benefits administration, taxes, and local compliance, offering support throughout a developer's tenure with your company to ensure engagement and retention.

Contact us to hire data engineers to streamline your data flows and ensure your company’s data integrity.

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Frequently Asked Questions

Is there a free trial period for hiring
Data Engineers
 
through Revelo?

Yes, if for any reason you find the developer you hire isn't a good fit within the first 14 days - you pay nothing or we can find you a replacement at no additional cost.

How are Revelo
Data Engineers
 
different?
Revelo offers full-time remote
Data Engineers
 
who share or highly overlap with your work day. You get world-class
Data Engineers
 
in Latin America who speak English and are vetted on soft and technical skills. All
live in the same time zones as the US or adjacent due to our talent base being exclusively in Latin America.
How do I hire
Data Engineers
 

Hiring a full-time developer through Revelo is a simple 3-step process. First, you tell us your hiring needs. Second, we match you to the best developers within 3 days. Third, you interview the candidates you like and hire the one you like most.

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