What Are the Largest Data Engineering Skills Gaps — and How Can I Solve Them?

In today's digital age, data is the lifeblood of organizations, and the ability to effectively manage and analyze it has become a crucial skill set. However, as the volume of data continues to grow, many organizations are struggling to find data engineers who possess the necessary skills to manage and analyze their data. In this blog, we'll explore the largest data engineering skills gaps and provide solutions to bridge those gaps.

Data Analysis

Data analysis is a critical skill that enables data engineers to make sense of vast amounts of data. However, many data engineers lack the skills needed to analyze data effectively. To bridge this gap, data engineers should focus on learning statistical analysis, data visualization, and machine learning techniques. Additionally, they should become proficient in programming languages such as Python, R, and SQL, which are essential for data analysis.

Data Filtering

Data filtering is another crucial data engineering skill. Filtering enables data engineers to identify and remove irrelevant data, ensuring that only relevant data is used in analyses. However, many data engineers lack the skills to filter data effectively.

To solve this gap, data engineers should focus on learning how to use data filtering tools such as Apache Spark and Apache Hadoop. Additionally, they should become proficient in SQL and programming languages such as Python, which are used in data filtering.

Data Presentation

Data presentation is the art of communicating complex data insights in a way that is easy to understand. Many data engineers struggle with data presentation, making it difficult for stakeholders to understand the insights generated from data analysis.

To bridge this gap, data engineers should focus on learning data visualization techniques such as Tableau, PowerBI, and matplotlib. Additionally, they should become proficient in programming languages such as Python, which are used in data visualization.

Data Governance

Data governance is the process of managing the availability, usability, integrity, and security of data used in an organization. Many organizations struggle with data governance, and data engineers play a crucial role in solving this gap.

To bridge this gap, data engineers should focus on learning data governance frameworks such as GDPR, HIPAA, and CCPA. Additionally, they should become proficient in data security and privacy best practices, as well as tools for managing data lineage and metadata.

Level up your data engineering skills today

As we've seen, there are several critical data engineering skills gaps that need to be addressed. To bridge these gaps, data engineers should focus on learning statistical analysis, data filtering, data visualization, data governance, and programming languages such as Python, R, and SQL. One effective way to acquire these skills is through a data engineering bootcamp. A data engineering bootcamp provides a structured learning environment that teaches participants the essential skills needed to become proficient in data engineering.

If you're interested in pursuing a career in data engineering, consider enrolling in a data engineering bootcamp offered by Pace.