Key Takeaways
- A data engineer transforms raw, unstructured data into organized, usable formats, making the role invaluable for businesses that rely on data-driven decisions.
- Core technical data engineer skills like SQL, ETL pipeline development, and programming are crucial for managing data, while additional expertise in machine learning (ML) or data warehousing can enhance efficiency for more complex, larger-scale data needs.
- Must-have soft skills, such as problem-solving, communication, time management, and adaptability, help data engineers collaborate, troubleshoot, and improve alignment with business goals, especially in remote work settings.
Your company has plenty of raw data, but your analysts spend more time cleaning up messy datasets than actually uncovering valuable insights. Reports take forever to generate, and as your business scales, the inefficiencies are only getting worse.
So now you need to hire a data engineer.
But hiring the best person for the job isn’t just about finding the candidate with the most experience—you need someone with the right data engineer skills to match your needs.
So, what exactly should you look for? In this article, we break down the essential technical and soft skills every data engineer should have to help you make an informed hire.
What Does a Data Engineer Do?
The worst thing a business can have is bad data. Raw data in its natural state isn’t ready for analysis, as it needs to be cleaned, organized, and structured first. That’s where a data engineer steps in.
A data engineer transforms raw data into usable information, helping businesses immediately address bad data. They build pipelines that automate data collection, storage, and processing, ensuring that analysts and data scientists have clean, structured information to work with.
Without a data engineer, companies risk making decisions based on unreliable data or wasting valuable time manually cleaning datasets.
There are also different types of data engineers. Some specialize in data pipelines, while others focus on machine learning (ML), data warehousing, and other areas of expertise. Taking the time to figure out what type you need will help you find the right fit.
Must-Have Skills of a Data Engineer
A strong data engineer skill set features both technical and soft skills. Whether hiring in-house or remotely, knowing these skills will help you find the best candidate for your business.
Essential technical skills for data engineers
Technical skills are the foundation of data engineering. These skills ensure data is managed in a way that makes it accessible and useful for decision-making.
Take note of these must-have data engineer technical skills:
- SQL and database management: A data engineer needs to work with structured data stored in databases like PostgreSQL, MySQL, and SQL Server. SQL (Structured Query Language) helps them extract, manipulate, and organize data efficiently.
- ETL and data pipeline development: Extract, Transform, Load (ETL) pipelines prepare raw data for use by cleaning and formatting it. Tools like Apache Airflow, Luigi, and Prefect help automate data workflows.
- Programming skills: Data engineers write code to process and integrate data. Python, Scala, Java, and Bash/Shell are commonly used for automation, data transformation, and system integrations. Python, in particular, is widely used due to its strong data-processing libraries.
- Experience with APIs for web scraping: Many businesses rely on external data sources. APIs allow engineers to collect data from different platforms, and web scraping extracts valuable insights from websites.
- Cloud computing and storage: Businesses increasingly rely on cloud platforms like AWS, Google Cloud, and Azure for data storage and processing. A data engineer must optimize cloud resources, manage storage costs, and scale as needed.
While not essential, there are also additional technical skills that can enhance a data engineer’s ability to support advanced analytics. This is especially true for businesses dealing with large-scale data processing.
Here are some nice-to-have data engineer technical skills:
- Big data technologies: Big data refers to the vast amounts of data businesses generate daily, and it’s reinventing business intelligence. Tools like Hadoop, Spark, and Kafka are needed for companies to handle complex and extensive analytics.
- Data modeling and warehousing: Knowledge of data warehousing solutions like Snowflake and Redshift isn’t always part of a data engineer’s core responsibilities and isn’t necessary for simple data needs, but having this can prevent performance bottlenecks and make data retrieval easier.
- Machine learning (ML) integration: While primarily handled by data scientists, engineers familiar with ML workflows can better support AI-driven applications. This bridges the gap between raw data and model deployment.
Essential soft skills for data engineers
Technical expertise is important, but soft skills are just as crucial. These personal attributes help individuals collaborate effectively and work toward project success.
And for those hiring a remote data engineer—which makes sense given that data engineering is a digital role—you’ll need a keen eye for assessing great soft skills as remote work heavily depends on written and verbal communication.
These are some must-have data engineering soft skills:
- Problem-solving: The most sought-after soft skill among IT professionals worldwide. A problem-solving mindset prevents costly errors and ensures data is always accessible.
- Good communication: Data engineers must explain technical concepts to non-technical teams. Remote engineers also rely on documentation, video calls, and written updates to stay aligned with the team.
- Collaboration and teamwork: Data engineers work closely with analysts, data scientists, and software engineers. Strong cross-team collaboration allows for smooth data flow between teams.
- Attention to detail: A minor mistake in data processing can lead to incorrect business decisions. Data engineers must be precise when writing queries, structuring databases, and managing pipelines.
- Business acumen: A solid grasp of business objectives allows engineers to structure data in ways that provide valuable insights for leadership teams. That way, engineers can create data solutions that are more relevant and impactful rather than just technically proficient.
- Adaptability: Technology evolves quickly, and data engineers need to learn new tools and techniques. Being open to change helps them (and your business) stay competitive and solve problems with the latest advancements.
Meanwhile, here are some nice-to-have data engineer soft skills. These aren’t strict soft skill requirements, but they help with adapting to changing business needs.
- Time management across time zones: Applicable to offshore remote data experts working with teams from other countries and US businesses. The ability to balance workloads while coordinating overlapping work hours with international teams helps keep everything on track.
- Creativity: Creativity can help data engineers design unconventional solutions or devise new ways to optimize data.
Beyond Skills: Practical Considerations When Hiring
When evaluating candidates, don’t overlook practical factors that can significantly impact day-to-day collaboration. Working hour overlap is particularly important for data engineering teams that need real-time communication.
A data engineer working in a time zone with substantial overlap to your core business hours allows for immediate troubleshooting, easier scheduling of meetings, and more seamless collaboration.
This becomes especially relevant if your business relies on real-time data processing or when implementing complex data infrastructure changes that require synchronous communication.
If you’re considering expanding your search globally to access a wider talent pool, time zone compatibility should factor into your decision-making process. Locations with at least a few hours of workday overlap can provide a good balance between access to global talent and practical collaboration needs.
For significant workday overlap, Latin America is your best choice. The region offers nearly complete time zone alignment with US business hours, allowing for real-time collaboration without the early morning or late night calls required when working with teams in Europe or Asia.
This practical advantage, combined with strong technical education systems and growing tech scenes in countries like Argentina, Mexico, and Brazil, explains why so many US companies are turning to LatAm for technical talent.
Final Thoughts
Hiring the right data engineer comes down to finding someone with both the technical chops to build robust data infrastructure and the soft skills to collaborate effectively with your team. The combination of these skills is what transforms raw data into valuable business insights that drive growth.
Given that data engineering is inherently digital work, your talent search doesn’t need to be limited by geography. Expanding your search beyond local markets opens access to a deeper talent pool and potentially significant cost savings.
For US companies, Latin American data engineers offer a compelling blend of technical expertise, cultural alignment, and and working hours that align with your business day. This practical advantage means no early morning calls or waiting until tomorrow for answers to urgent questions.
If you’re looking to hire a remote data engineer and want to learn more about tapping into Latin American talent, check out our article “Why You Should Hire Nearshore Data Engineers and How to Do It.”
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