Data Engineering Skills and Requirements for a Thriving Career

Data engineers are in high demand, making the field attractive to professionals looking for an exciting career opportunity. For those who want to become a data engineer or are interested in spurring data engineer career growth to progress in the niche, a computer science or similar degree isn’t enough alone. Possessing the key skills for a data engineer is an essential part of the equation, too.

Here’s an overview of the must-have capabilities and requirements for a thriving career.

Basic Data Engineering Skills

Data engineer basic skills are usually the capabilities required to break into the field and secure a junior or similar entry-level role. Usually, these are the abilities even freshers require, as they encompass the fundamentals of the field. Here’s a breakdown of what’s needed.

Programming Languages

Python or/and Java

Programming skills are critical in many tech professions, and engineers must have them without question. Among the core data engineering skills needed in this area are Python or Java. Generally, a strong understanding of at least one of those languages is a requirement, though familiarity with both is potentially ideal. By being able to use Python and Java effectively, it’s easier to access a broader selection of job opportunities, and that can elevate a professional’s career.

SQL

The role of a data management professional usually requires significant knowledge of SQL, making it one of the most critical data engineer technical skills in the equation. SQL allows professionals to navigate and harness highly complex datasets when it’s used at the appropriate level. Additionally, it can make constructing warehouses and integrating them with a variety of tools easier to manage. As a result, it’s critical to go beyond basics like standard queries while honing the capability, ensuring professionals can leverage SQL effectively while performing the duties associated with their role.

Database Management

SQL Databases

Since SQL is commonly one of the data engineer requirements, it shouldn’t be a surprise that knowledge of SQL databases is similarly critical. In fact, it’s one of the most in-demand capabilities for professionals in the field, as SQL is the go-to option for relational databases.

Along with understanding how SQL databases are structured and used, supporting knowledge of database management is crucial. With that, professionals are commonly more effective in their roles, making them a greater asset to their employers.

NoSQL Databases

Another of the necessary skills is NoSQL databases. While SQL databases focus on structured data, NoSQL databases can incorporate semi-structured and unstructured data into the mix. In a landscape where companies increasingly want to harness a wide array of information types to derive insights for data-driven decision-making, that makes NoSQL not a “should have” capability but a genuine must-have for professionals looking to launch successful careers.

Data Warehousing Solutions

Amazon Redshift

A robust data engineer skill set includes knowledge of tools that support data storage and analysis, including data warehouse solutions like Amazon Redshift. Redshift is a cloud-based relational database query and management platform that works alongside Amazon Web Services (AWS), specifically S3 buckets and EC2 instances. It’s capable of handling massive volumes of information and processing multiple kinds of information. With the popularity of AWS, adding Redshift to a resume can make a significant difference, ensuring professionals can land lucrative opportunities with greater ease.

Google BigQuery

Another of the data engineer essential skills in the warehouse category is Google BigQuery. It’s a cloud-based option for managing and analyzing substantial quantities of information. Additionally, it has several cutting-edge features, including machine learning and geospatial analysis capabilities. It can even conduct federated queries to incorporate external information from outside sources. As a result, BigQuery is a highly popular solution, particularly among companies using Google Cloud. That makes it a prime skill to learn for anyone interested in the field.

Snowflake

Snowflake is another of the skills that’s worth considering. While it’s not as widely used as Redshift or BigQuery, it’s a cloud-based, low-maintenance option with plenty of merit. It offers separation between storage and computing functions, reducing the need for multiple copies of the information to handle cross-departmental needs. Like competitors, it’s also highly scalable and inherently flexible, and many feel it’s easier to use. Since that’s the case, it’s an increasingly popular solution among businesses, which is why learning it is potentially beneficial.

Advanced Skills for Data Engineers

For professionals looking for a method to improve their careers or reach senior positions with greater ease, developing a more advanced data engineer skillset is the best move. Generally, this involves acquiring capabilities beyond what’s expected of those new to the field, allowing professionals to handle more complex tasks and offer additional value. Here is a look at some advanced skills worth building for engineers looking to elevate their careers.

Big Data Technologies

Hadoop

In the world of data science, specific skills are in high demand. In this area, one of the skills required for a data engineer is Apache Hadoop. Apache Hadoop is an open-source framework that’s used to store and analyze information on a large scale. It’s not a platform. Instead, it’s a toolset that supports data integration, making it valuable for analytics. Overall, Hadoop is incredibly powerful but also very low-cost, which is why it’s popular. It’s particularly adept at managing computationally complex situations, such as those used in iterative algorithms or batch processing.

Spark

Apache Spark is a leading real-time processing framework. Like Hadoop, Spark has long been popular, and the two technologies are often harnessed together. With Spark, the focus is generally on processing. It’s suitable for large-scale datasets and works for distributed processing activities across multiple computers. Many companies also favor Spark since it’s an open-source solution, as well as its ability to work for batch processing or real-time analytics.

Kafka

Another open-source solution, Apache Kafka is primarily used for data pipelines, integration, and stream processing. It’s a scalable solution that offers low latency, and its Connect interface can integrate with a wide array of even sinks and sources. With Kafka, it’s possible to store, process, publish, and subscribe to record streams in real time, including at high volumes. Ultimately, that makes Kafka a popular tool, which is why it’s a wise skill to develop for anyone interested in engineering.

ETL Processes

ETL Basics

Extract, transform, load (ETL) processes play a significant part in the creation, maintenance, and optimization of pipelines. As a result, understanding the ETL basics is one of the critical skills needed for a data engineer.

ETL pipelines essentially pull information from a specific source – like a designated database – and move it to another. Once complete, the retrieved information is available for analysis, reporting, and generating critical insights. Along with gathering new information, ETL processes also assist with migration, which is another reason why it’s a highly desirable skill.

ETL Tools

Apache Nifi

One of the primary skills related to ETL tools professionals need is Apache NiFi. NiFi is a platform that allows for the automation of ETL processes, allowing information to move between systems with greater ease and less manual interaction. Plus, NiFi offers real-time control over the movements and uses a streamlined drag-and-drop interface, which makes directing information from various sources to different destinations simpler to do. It’s also possible to run NiFi on one server or across a cluster, making it versatile and scalable.

Talend

Another of the relevant skills related to ETL tools is Talend. It simplifies integration by offering an intuitive interface and robust capabilities. Talend also has features to support data quality and includes automation for enhanced efficiency. Compared to other ETL tools, Talend is lower cost, which is why it’s popular with many companies looking to leverage collected information without breaking the bank.

Cloud Computing Platforms

AWS

Cloud platforms are widely used for storage and processing due to their flexible, scalable nature. AWS is definitively one of the most popular options, making it one of the cloud data engineer skills required by many employers. Generally, understanding how to design and deploy infrastructure in AWS is essential. Familiarity with associated services – like Redshift, EC2, and RDS – is similarly critical.

Azure

Microsoft Azure is another of the data engineer top skills for storage and processing. An understanding of Azure SQL Database, Azure Data Lake, and Azure Cosmos DB is incredibly beneficial. These capabilities allow professionals to support company goals, including ensuring high availability and suitable storage and processing pipeline abilities.

Google Cloud

Another of the data engineer must-have skills in the cloud computing niche is Google Cloud, a highly popular cloud platform widely used by businesses of all sizes. The broader solution includes Google BigQuery for warehousing. Plus, there’s scalable storage options that provide high degrees of availability, which is ideal for real-time analytics and supporting various connected applications. Plus, knowledgeable engineers can harness the platform to provide machine learning capabilities and automation for greater efficiency.

Data Modeling and Architecture

Among the data engineer required skills are modeling and architecture. Knowing how to create conceptual representations of collected information ensures organizations can properly plan for storage and analysis needs. With architecture know-how, it’s easier to design, build, and maintain systems using a strategic approach that aligns with organizational goals.

Here’s an overview of some of the needed capabilities related to modeling and architecture.

Data Modeling Concepts

Among these skills, understanding the core concepts surrounding building data models is a must. By learning the right techniques, professionals can design roadmaps for the creation of scalable, optimized warehouses and databases. Along with knowing general design techniques, familiarity with constraints, relationships, and scalability is essential to excel in this area.

Data Pipeline Design

As a data platform engineer, skills related to pipeline design are essential. Pipelines are functionally a series of components that automate the gathering, organization, moving, transformation, and processing of information as it shifts from its origin to a destination. Essentially, the process ensures that companies can turn raw data into something leverageable, making it easier to use it for intelligent decisions.

Data Governance and Quality

Two more of the critical data engineer job skills are governance and quality assurance. Since the field usually involves integrating information from multiple sources to support future analysis, having mechanisms in place to safeguard information in transit, limit access, ensure security, and support privacy requirements is essential. Similarly, maintaining integrity is a must, ensuring reliability and consistency.

In the simplest sense, professionals need to know how to make sure that information is accurate and trustworthy, as well as properly controlled to prevent incidental or intentional tampering. Both governance and quality play a role in that equation, which is why they are crucial capabilities for those looking to advance in the field.

Machine Learning Basics

One of the newer data engineer skills needed to thrive in the field is machine learning. With machine learning algorithms, modeling and processing are dramatically simplified. The algorithms can often extract meaningful insights with limited manual interaction. Plus, they can often adapt over time, making them increasingly capable as more information is gathered and processed.

At a minimum, specialists in this field should learn the basics of machine learning. However, more advanced levels of understanding aren’t just beneficial; they often serve as a solid differentiator, which can make it easier to elevate one’s career to new heights.

Data Analysis and Visualization Basics

Nearly all data analysts and engineers would attest that they need data visualization software and data analytics skills to thrive professionally. As a result, it’s best to treat both as essential skills for a data engineer.

When it comes to the exact data engineer skills and tools required to support analysis and visualization, it can vary depending on the precise role. In many cases, familiarity with business intelligence (BI) tools and Python libraries is part of the equation. Here’s a look at some of the skills related to BI tools and Python libraries.

BI Tools

Tableau

When it comes to data engineering tools and skills, Tableau is a system worth knowing. Tableau is a visual analytics platform that makes managing and exploring collected information and gathering insights easier. As a result, it’s a popular solution for decision-making.

PowerBI

Another of the main skills for analysis and visualization is Microsoft Power BI. This visualization platform allows analysts and other professionals to take large amounts of data and generate useful insights. There are multiple visual output options available, including maps, graphs, charts, scatter plots, and more. AI is also a feature of the program, adding functionality for improved insights.

Looker Studio

For professionals, familiarity with Google Looker Studio is helpful. The tool allows users to take large amounts of information and create visualizations and reports. It’s designed with large enterprises in mind and is often favored in workplaces that utilize Google Cloud services.

Python Libraries

Pandas

When it comes to Python libraries, Pandas is one of the data engineer key skills that’s worth having at the ready. It’s an open-source library that allows professionals to analyze information in Python. Typically, it takes information stored in SQL databases, CVSs, or similar sources and creates a frame to make conducting analysis easier.

Matplotlib

For professionals in data engineer roles, another Python library worth knowing is Matplotlib. It’s designed to help with the creation of visualizations in Python, including interactive and animated ones. While it’s mainly used for plotting functions and displaying curves, it also has other capabilities.

Seaborn

Another one of the data engineer needed skills is Seaborn. This Python library focuses on tools for creating statistical graphics in Python. Its robustness is comparable to Matplotlib, but it offers unique capabilities and an inherent simplicity that make it worth using for exploration.

Plotly

With Plotly, professionals can access tools to create more than 40 unique chart types to visualize information. It’s an open-source option that produces interactive graphics, and the visuals are embeddable on websites if that’s needed. As a result, knowing how to use Plotly is worthwhile for anyone in the field.

Soft Skills for Data Engineers

Handling positions in this field effectively requires more than technical prowess. Ultimately, specific traits and characteristics are functionally crucial for performing optimally and navigating workplace relationships. So, while hard skills or tech skills are undeniably critical abilities data engineers must have, soft skills are also some of the most important capabilities to possess.

For professionals in data engineering roles, one of the most essential soft skills is problem-solving. Critical thinking is similarly vital. Additionally, organization, time management, and planning skills are highly useful, particularly once professionals begin handling projects independently.

Since engineers are responsible for working as part of a team, communication and collaboration are also essential. The ability to share complex information with less tech-savvy individuals is also a must, as it allows professionals to work well with a variety of colleagues and stakeholders.

In many cases, presentation skills provide value to engineers, as it allows them to showcase the results of their efforts with greater ease. Creativity can also prove valuable. Along with making it easier to solve problems, creativity may allow a professional to harness available information in a new way, making unexpected insights accessible.

If you’re aiming to advance your career beyond the individual contributor level, then don’t overlook the power of well-developed leadership capabilities. Delegation and coaching can prove valuable later in a professional’s career, too.

Overall, these non-data-related aptitudes are primarily traits and characteristics a professional possesses. However, while they aren’t usually taught through formal courses, soft skills are something aspiring professionals can learn through hands-on experience and self-awareness.

Pet-project Ideas to Improve Data Engineer Skills

Since the skills above are largely required to become an engineer, professionals need a way to hone the right capabilities. While gaining experience on the job is often ideal, taking on personal projects is another option. By pursuing a pet project, professionals can get hands-on experience in the right areas. Plus, it gives them powerful additions to their portfolio, which can make securing a career-launching or career-boosting position easier.

There are several straightforward options that can serve as excellent starting points. For example, scraping information from social media, stock value publications, real estate listings, aviation logs, or similar places can allow professionals to create suitable data sets. Then, they can analyze the information using the skills and tools above to gain meaningful experience.

When scraping, aggregating information from several sources to create a single, unified resource is also worth doing. Similarly, designing visualizations to demonstrate any discoveries allows professionals to develop skills in that area and highlight anything they found when doing the project.

One thing to keep in mind when selecting potential information to work with is personal interest. If a professional is undeniably interested in the insights a project will generate, it’s far easier to remain motivated during the learning process, and that often leads to better results.

Conclusion

Ultimately, the demand for data scientists and engineers is continuing to rise, and that trend isn’t likely to change any time soon. For those who want the best career possible, the data engineer skills list above is a solid starting point. It can help entry-level professionals, mid-careerists looking to move up, aspiring managers, and existing managers progress in the field.

Precisely how a professional acquires the capabilities above is potentially up to them. Formal education, self-directed learning, on-the-job training opportunities, and a slew of other avenues are all worth considering. Plus, taking on pet projects is wise, as it helps professionals hone skills while boosting their portfolio.

With a bit of dedication and hard work, nearly anyone can develop the engineering skills necessary to thrive. Use the information above as a guide. That way, you know you’re moving in the right direction.