Data Engineering 101: Introduction to Data Engineering

Data engineering has been termed as a career advancement stage, mainly for professionals with backgrounds in Software Engineering, Data Science, and/or Business Intelligence analysis. The question however is, can an individual take data engineering as an entry-level role?
Data Engineering is the practice of Designing and building systems for collecting, storing, processing, and analyzing data at scale. The main aim of data engineering is to make quality data available for analysis and efficient data-driven decision-making. A data engineer is responsible for making quality data available from various sources, maintaining databases, building data pipelines, querying data, data preprocessing, feature engineering, Apache Hadoop and spark, and developing workflows among other tasks as may be required by the organizational needs.
The skills required for one to become a data engineer include:
Programming skill. The data engineer is required to be conversant and with adequate knowledge of a `data-engineering-specific programing language. Some of the most advocated programming languages for data engineering include Python, Java, and Scala.
Big Data Skills. The data Engineer is expected to be conversant and familiar with these big data tools: Spark, Hadoop and MapReduce, Apache Hive, and Pig Sqoop.
Databases Skills. The data Engineer is expected to have extensive SQL knowledge. The database skills should cover handling and maintaining databases, creating schemas, and performing querying operations on the databases. The data engineer is expected to understand the best databases for transactions and those preferred for analysis.
Distributed systems Skills. The data engineer is expected to have experience with distributed file systems like Hadoop and have the ability to tackle issues involving large amounts of data.
Cloud Computing skills. The data engineer is required to understand and perform the different operations on various cloud platforms. With most application workloads migrating to various cloud platforms, its therefore for an in-depth understanding of the cloud systems.
Data warehousing skills. The Extract, Transform, Load (ETL) operations are among the key responsibilities of a data engineer. Therefore, the data engineer needs to learn how to design, build and operate a Data Warehouse.
Data Engineering role as an entry-level role is a possibility and very much attainable. The path to this will involve building proficiency in programming languages like Python, R, Java, and SQL. Developing knowledge and a deep understanding of databases and Big Data skills will cement the data engineering career. A basic understanding of Distributed file systems, Cloud computing, and Data Warehousing skills will also be critical. Building proficiency in the use of different tools and platforms, ensuring at least advanced skills and knowledge in one under each category will guarantee a strong combination of skills for data Engineering.
Putting into practice the data engineering skillset while continuously building on soft skills will go a long way toward a soft landing for newbies to data engineering as an entry-level role.