A career in data engineering requires extensive analytics abilities, such as those needed by data scientists, on the one side and programming and software engineering, on the other.

Strong programming abilities, a comprehension of big data technology, a working knowledge of statistics, and analytical capabilities are all necessary for success in data engineering. 

Reading this guide, you may learn more about this intriguing career path and the abilities you'll need to develop.

Who is a Data Engineer?

A data engineer is a professional responsible for designing, building, maintaining, and testing data pipelines. 

These pipelines extract data from various sources, process and transform the data, and then load the data into storage systems or data warehouses for further analysis and reporting.

Data engineers typically have a strong background in computer science, software engineering, and databases and are proficient in programming languages and tools such as Python, SQL, and Apache Spark. 

They also have experience with data storage and management technologies such as Hadoop, NoSQL databases, and data warehouses.

What does a data engineer do?

A data engineer is responsible for designing, building, maintaining, and testing data pipelines. These pipelines extract data from various sources, process and transform the data, and then load the data into storage systems or data warehouses for further analysis and reporting.

• Extracting data from various sources: Engineers extract data from multiple sources, such as databases, flat files, and APIs. They may also be responsible for integrating data from various sources and ensuring that it is appropriately formatted and cleaned before loading it into storage systems or data warehouses. Some specific tasks that a data engineer might do include:
• Transforming and cleaning data: Data engineers change and clean the data to make it suitable for analysis and reporting. This may involve removing duplicates, filling in missing values, or aggregating data.
• Loading data into storage systems: Data engineers load the data into storage systems or data warehouses, such as Hadoop, NoSQL databases, or data warehouses like Amazon Redshift or Google BigQuery.
• Building and maintaining data pipelines: Data engineers make and maintain data pipelines to ensure that data is delivered to the right place at the right time. They may use tools such as Apache Spark or Apache Beam to process and transform the data as it moves through the pipeline.
• Testing and debugging data pipelines: Data engineers test and debug data pipelines to ensure they function correctly. They may use tools such as JUnit or PyTest to write and run automated tests, log files and other debugging techniques to troubleshoot issues with the pipeline.

Programs a data engineer should be familiar with

Data engineers should have a strong foundation in computer science and be proficient in various programming languages and tools. 

Some essential programs and technologies that a data engineer should be familiar with include the following:

• Programming languages: Data engineers should be proficient in at least one programming language, such as Python, Java, C++, or R. Many data engineers also have experience with SQL, commonly used for data manipulation and querying.
• Data storage and management technologies: Data engineers should be familiar with Hadoop, NoSQL databases, and data warehouses. They should also have experience with tools like Apache Spark and Apache Beam, which process and transform data as it moves through a pipeline.
• Data integration and ETL tools: Data engineers should be familiar with tools such as Apache Nifi, Talend, and Pentaho, which extract, transform, and load data from various sources.
• Version control systems: Data engineers should be familiar with version control systems such as Git, which track changes to code and manage projects with multiple developers.
• Cloud computing platforms: Data engineers should have experience with such platforms as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. They should be familiar with tools and services such as Amazon S3, Amazon EMR, and Google BigQuery, commonly used for data storage, processing, and analysis.

In addition to these technical skills, data engineers should also have strong problem-solving and communication skills. They often work on complex projects and collaborate with data analysts, scientists, and other stakeholders.

Collaborating with data analysts and data scientists: Data engineers often work closely with data analysts and scientists to understand their data needs and help them access and analyse the data. 

They may also assist with developing machine learning models and other data-driven projects.

How to become a data engineer

There are several steps you can take to become a data engineer:

• Obtain a bachelor's degree in a related field: A bachelor's degree in computer science, data science, or a related field is a good foundation for a career as a data engineer. Coursework in programming, data structures, algorithms, and databases will be beneficial.
• Gain experience with relevant technologies: Data engineers should have experience with various programming languages, data storage and management technologies, and data integration and ETL tools. You can gain this experience through internships, online courses, and personal projects.
• Develop strong problem-solving skills: Data engineering involves solving complex problems and working with large datasets. You can improve your problem-solving skills by practising and working on challenging projects.
• Learn about data pipelines and warehousing: Data pipelines and warehousing are essential concepts for data engineers to understand. You can learn about these topics through online courses, books, or by working on projects that involve building data pipelines or working with data warehouses.
• Get certified: Earning a certification in a relevant technology, such as the AWS Certified Data Engineer or the Google Cloud Certified Professional Data Engineer, can help you stand out in the job market and demonstrate your expertise to potential employers.
• Build a strong portfolio: As you gain experience and skills, it is essential to document your work and build a strong portfolio showcasing your data engineer abilities. This can include projects you have worked on, code samples, and any relevant certifications or awards.

Finally, consider joining professional organisations and networking with other data professionals to learn about job openings and stay up-to-date on industry trends.

Career Paths for a Data Engineer

Data engineers can pursue several career paths depending on their skills, interests, and goals. Some options include:

• Data engineer at a large corporation: Data engineers at large corporations may work on projects related to data warehousing, data pipelines, and data integration. They may also collaborate with data analysts, data scientists, and other stakeholders to understand their data needs and help them access and analyse data.
• Data engineer at a startup: Data engineers at startups may have a broader range of responsibilities, as they may be responsible for building and maintaining the company's entire data infrastructure. They may also work closely with the product development team to understand the data needs of the business and help incorporate data into the product.
• Data engineer in the public sector: Data engineers in the public sector may work for government agencies, non-profit organisations, or educational institutions. They may be responsible for building and maintaining data pipelines and warehouses, collaborating with data analysts and other stakeholders to understand their data needs and help them access and analyse data.
• Data engineer in consulting: Data engineers work with various clients to help them design and implement data infrastructure solutions. They may work on projects related to data warehousing, data pipelines, and data integration and provide training and support to clients.
• Data engineer in academia: Data engineers in academia may work in research labs or other academic settings. They are responsible for building and maintaining data pipelines and warehouses and collaborating with researchers to help them access and analyse data.

Regardless of the specific career path, data engineers typically have strong technical skills and a deep understanding of data storage and management technologies, data pipelines, and data warehousing. 

They also have strong problem-solving and communication skills, as they often work on complex projects and collaborate with data analysts, data scientists, and other stakeholders.