16 must-have data engineer skills

Data engineering has become a critical function in ensuring that data pipelines are efficient, reliable, and scalable. The role of a data engineer is vital in this landscape, bridging the gap between raw data and actionable insights.

In this article, we'll explore what a data engineer does, why data engineering is crucial for businesses, and the essential skills—both technical and soft—that every data engineer should know. We'll also examine how tools like dbt Cloud can help data engineers excel in their roles.

What is a data engineer?

Simply put, a data engineer is responsible for designing, building, and maintaining the infrastructure that supports the collection, storage, and transformation of data. This infrastructure includes data pipelines that move data from various sources into a central data warehouse or data lake where it can be analyzed.

Data engineers ensure that this flow of data is efficient, error-free, and scalable, enabling data scientists and analysts to focus on generating insights. They often work with large datasets and are expected to build systems that can handle both structured and unstructured data.

Why is data engineering important?

Data engineering is crucial because it ensures that data is accessible, accurate, and reliable. Without proper engineering, data can become a bottleneck, slowing down analytics and impeding decision-making. In an age where data-driven decisions are key to staying competitive, organizations rely on data engineers to maintain the health and efficiency of their data ecosystems.

With the rise of machine learning and artificial intelligence, having clean, well-organized, and structured data has never been more important. Data engineers make this possible by building robust data pipelines, often using both ETL (extract, transform, load) and ELT (extract, load, transform) methods to move and structure data.

Technical data engineer skills

1. SQL proficiency: Structured Query Language (SQL) is the backbone of most data engineering work. Data engineers need to be adept at writing efficient SQL queries to manipulate and retrieve data from databases. SQL is essential for working with relational databases like PostgreSQL, MySQL, and others.
2. Data warehousing: Understanding data warehouse design and architecture is fundamental for data engineers. A good grasp of how data is stored, organized, and accessed in data warehouses (like Snowflake, Redshift, or BigQuery) allows engineers to optimize data retrieval for analytics.
3. ETL and ELT frameworks: Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) frameworks are crucial for building data pipelines. ETL involves extracting data from source systems, transforming it to fit the data model, and loading it into a target system. ELT, on the other hand, focuses on loading raw data into the target system first and then transforming it. Both approaches have their use cases, and understanding when to apply each is critical for data engineers. Learn the primary differences between ELT and ETL.
4. Programming languages: Data engineers should be proficient in at least one programming language, with Python and Java being the most common. Python, in particular, is favored due to its versatility and the rich ecosystem of libraries like Pandas and NumPy, which are useful for data manipulation and transformation.
5. Cloud platforms: Cloud infrastructure plays a significant role in modern data engineering. Familiarity with platforms like AWS, Google Cloud, or Microsoft Azure is essential, as more companies are moving their data workloads to the cloud for scalability and cost-effectiveness. Knowing how to set up and manage services like S3, Redshift, or Google BigQuery is invaluable.
6. Data modeling: Data modeling skills allow engineers to define how data is organized within databases. Engineers need to know how to design efficient schemas, choose appropriate data types, and understand normalization vs. denormalization. This ensures that data is stored in a way that is easy to query and analyze.
7. Data governance and security: As organizations deal with growing volumes of sensitive data, security and governance are becoming increasingly important. Data engineers must implement best practices for data encryption, masking, and role-based access control. They also need to be familiar with compliance regulations, such as GDPR, to ensure that data is handled appropriately.
8. Automation and orchestration tools: Managing data pipelines manually is time-consuming and error-prone. Tools like Apache Airflow or dbt Cloud can automate and orchestrate these processes, ensuring data flows seamlessly between systems. Data engineers should understand how to set up and manage these tools for pipeline automation.
9. Big data tools: Handling massive datasets requires specialized tools. Data engineers should be familiar with technologies like Hadoop, Spark, and Kafka, which are designed to process and manage large volumes of data in real-time or batch processing environments.
10. API integration: Many data pipelines require extracting data from APIs. Understanding how to work with RESTful APIs and tools like Postman is essential for building robust pipelines that can pull data from third-party sources.

Soft data engineer skills

1. Communication: Data engineers often work in cross-functional teams with data scientists, analysts, and business stakeholders. Strong communication skills are essential for understanding requirements and explaining technical concepts to non-technical stakeholders.
2. Problem-solving: The ability to troubleshoot and solve complex problems is crucial in data engineering. Whether it's debugging a failing pipeline or optimizing a slow-running query, data engineers need to approach problems with creativity and persistence.
3. Collaboration: Data engineers need to work closely with data analysts, data scientists, and IT teams. Strong collaboration skills ensure that everyone is aligned and that data infrastructure supports the broader business goals.
4. Adaptability: The data landscape is constantly evolving with new technologies and methodologies. Data engineers must be adaptable and open to learning new tools, frameworks, and techniques to stay current in the field.
5. Attention to detail: Data engineers must be detail-oriented, as even small errors in a data pipeline can lead to incorrect analyses and flawed business decisions. Ensuring data integrity and accuracy is paramount.
6. Project management: Data engineers often manage multiple projects simultaneously, from building new pipelines to maintaining existing infrastructure. Having strong project management skills allows engineers to prioritize tasks, meet deadlines, and ensure smooth delivery of projects.

How dbt Cloud helps data engineers do their best work

Data engineers can use dbt Cloud to transform raw data into actionable insights more efficiently. While dbt Core is an open-source tool that focuses on transformation within the ELT process, dbt Cloud enhances this by providing additional features like automated workflows, version control, and collaboration tools.

By using dbt Cloud, data engineers can automate much of their pipeline, reducing the need for manual intervention. It integrates seamlessly with existing data warehouses, making it easier to manage large-scale transformations without overcomplicating workflows. dbt Cloud also provides robust monitoring and alerting features, ensuring that pipelines are always running smoothly.

Additionally, dbt Cloud’s ability to enforce data testing and documentation helps ensure the quality and reliability of the data, which is critical for maintaining trust in the outputs of analytics and machine learning models.

Conclusion

The role of a data engineer is both challenging and rewarding, requiring a combination of technical and soft skills to succeed. From mastering SQL and data warehousing to honing communication and problem-solving abilities, there are many essential skills that every data engineer should possess.

Tools like dbt Cloud play a crucial role in helping data engineers manage the complexities of modern data pipelines, making their work more efficient and scalable. As data continues to play an increasingly central role in business decision-making, the demand for skilled data engineers will only grow.

Sign up for a free dbt Cloud account today and level-up your data engineer skillset.