9 Data Engineering Skills To Stand Out From The Crowd

Today, businesses increasingly rely on data for decision-making to gain a competitive advantage. This reliance has highlighted the need for individuals who can manage, analyze, and derive insights from this data. Data engineers play a significant role in managing these large chunks of data to enable efficient data processing. And as the demand for data engineers continues to grow, you can differentiate yourself from the crowd by developing a comprehensive skill set that can enhance your prospects for success.

This article will help you understand data engineering, data engineering skills, data engineers' roles and responsibilities, and some career opportunities.

What is Data Engineering?

Data engineering is the practice of designing, building, and maintaining the data infrastructure for business intelligence and decision-making. It involves collecting, processing, transforming, and interpreting large amounts of data and making it accessible for analyses or other downstream applications. 

Who is a Data Engineer?

A data engineer is a person who bridges the gap between raw data and valuable insights, enabling the business to derive value from datasets. They lay down the foundation for handling big data using different tools. In other words, data engineers are responsible for designing the system for managing and processing big data.

What are the Roles and Responsibilities of a Data Engineer?

Data engineering professionals' roles and responsibilities may vary according to the industry and organization in which they work. They are responsible for designing, building, and maintaining the data infrastructure supporting organizational data processing, storage, and analysis.

Let’s look at some of the common roles and responsibilities of a data engineer: 

• Designing Data Architecture: They design, build, and maintain the data infrastructure that forms the backbone of data collection, storage, processing, and analysis. Data engineers select the appropriate data storage solutions and models. This choice directly impacts the ability to optimize storage and processing techniques, ensuring efficient data retrieval for analysis. Furthermore, they also set standards to safeguard sensitive information, ensuring data quality.‍
• Data Collection: The process of data engineering enables data engineers to gather data from various sources, such as databases, files, streaming platforms, IoT devices, APIs, and external sources. ‍
• Data Processing: Usually, the collected data is unstructured, which needs to be cleansed to meet the specific needs of targeted systems or analyses. This may include handling missing values or fixing inconsistencies.  ‍
• Data Loading: Depending on the process, the data engineers load raw or transformed data into the targeted storage systems. The destination could be data lakes, warehouses, or database systems.‍
• Data Security and Compliance: Data engineers implement security measures and high-level encryption standards to protect sensitive data.‍
• Collaboration: A data engineer must communicate with cross-functional teams and stakeholders, including data scientists, analysts, etc. They must understand the data needs and ensure successful data integration and analysis.

9 Essential Data Engineering Skills You Need

Let’s look at some data engineering skills you must possess to excel in the field:

Technical Skills

To build a successful career as a data engineer, you must be able to design, build, and understand the core of data infrastructure by continuously learning about new technologies. 

Let’s look at some important technical data engineering skills needed to make you stand out from the crowd.

Programming Skills

• Python: Python is a flexible and easy-to-read programming language. This can be a useful skill set for performing data extraction, manipulation, and more.‍
• SQL: SQL is used to query databases. It helps retrieve data from specific databases, filter it, and perform operations like inserting, deleting, and updating the data. Being well-versed in SQL will help you efficiently design, implement, and manage databases, write complex queries to extra-specific data, and more.

Knowledge of programming languages makes you flexible and versatile when handling different types of data. 

Machine Learning Skills

Machine learning (ML) is a branch of artificial intelligence that focuses on building algorithms and statistical models. These models enable computers to learn and predict outcomes without explicit programming. By understanding the concept of ML, data engineers can leverage ML techniques to perform tasks within their workflows. These tasks can include data preprocessing, anomaly detection, image or video processing, predictive analysis, etc. Although machine learning skills are linked with data scientists, data engineers can benefit from this technique while handling big data. 

Understanding Database Storage and Management Systems

A data engineer should be familiar with different storage systems, including relational databases (SQL or PostgreSQL), NoSQL databases (MongoDB or Cassandra), data warehouses, and data lakes. Understanding each storage system's strengths and limitations will help engineers choose the best option according to the work requirements.

Knowledge of Big Data Technologies

Big data technologies play a crucial role in today’s world of massive datasets. Technologies such as Apache Spark and Hadoop are frameworks designed to handle a large amount of structured and unstructured data. They address challenges related to storage, processing, and speed. Understanding how these frameworks work will help data engineers implement data pipelines in modern-day ecosystems for high performance. 

Cloud-Computing Understanding

Cloud computing refers to using services such as servers, storage engines, software, and more over the cloud. It is a cost-effective technology that offers flexibility, storage capability, and scalability. Data engineers should be proficient in understanding cloud computing technologies so that they can effectively use and implement different cloud solutions available today.

Expertise in Data Integration Processes

Data doesn’t always reside in a single location. Data engineers must be experts in data integration, the process of combining data from various sources, transforming it, and loading it into a target system for analysis and reporting. This ensures all the organization's data can be effectively organized.

While data integration is crucial, it can be challenging when data from different sources often has different formats and structures. To overcome these challenges, engineers can leverage various data integration tools and platforms that will help automate the process. Being familiar with data integration implementation ways—ELT and ETL would be a cherry on the cake! This will help to choose the most efficient approach for data integration tasks. 

One reliable and robust data integration and replication tool that can address data engineering challenges is Airbyte. Airbyte offers several advantages for data engineers: 

• It offers over 350+ pre-built connectors to popular sources and destinations, streamlining the connection process.
• Airbyte supports dbt integration to simplify complex data transformation tasks with dbt models.
• With its Connector Development Kit (CDK), data engineers can design customized connectors in a breeze! This allows the development of tailored solutions according to specific needs, connecting to any data sources and destinations.
• The Airbyte connectors follow SSL, SSH, and HTTPS security channels to transfer data from the source to the destination, ensuring optimal data security.

Soft Skills

Along with a technical data engineering skillset, you should have some soft skills to communicate your ideas effectively and collaborate with team members to solve problems. This will help you to adapt to the work environment and deliver value to the organization.

Let’s look at some soft skills for data engineering: 

Communication and Collaboration 

A data engineer often needs to communicate with teams from different departments to convey technical concepts and share ideas for planning and execution, either verbally or through presentation. Clear communication will foster a better understanding and alignment between you and your team.

Problem-Solving

You must be able to identify critical thinking problems, troubleshoot data pipeline issues, and debug codes, ensuring data quality. Strong problem-solving skills will enable you to improve data quality across the organization, address performance bottlenecks, and improve work efficiency. 

Adaptability and Continuous Learning

A data engineer must be able to adapt and learn quickly about new tools and technologies and stay updated with industry trends. These skills are crucial for effectively responding to emerging opportunities and challenges.

Top Data Engineering Career Opportunities

There are numerous career opportunities for a professional who is looking to start their career in the field of data engineering. Let’s look at some of the career options: 

• Data Engineer: The data engineer profession is a foundational aspect of data engineering. As a data engineer, you will work with a variety of databases and cloud platforms to ensure data is collected, transformed, and loaded for further analysis. ‍
• Big Data Engineer: This position specializes in handling large data sets consisting of structured and unstructured data using different technologies, such as Apache Hadoop or Spark. You design and implement scalable storage, data processing, and analysis solutions. ‍
• Cloud Data Engineer: Cloud data engineers design and maintain large repositories optimized for handling enormous datasets. They ensure optimized data operations, such as setting up storage solutions, efficient data retrieval, etc. ‍
• Data Governance Specialist: Data governance specialists are responsible for establishing data governance policies and standards. They work closely with data analysts, stewards, and compliance teams to ensure data privacy and regulatory compliance throughout the data lifecycle.

Conclusion

Data engineering is a field that doesn’t only involve laying the foundation for data architecture or infrastructure but also managing and processing the data according to the business needs. Today, we are surrounded by ample data, which could sometimes be harder to access because of its complexity. That’s where data engineers come in to help and manage this data using unique data engineering skills. By developing the above-mentioned skills, you can enhance your career as a data engineer and learn how to resolve complex data challenges and derive innovative solutions.