Data Engineering is a field in Data that involves the practice of designing architectures and building systems for sourcing data, storing it and analyzing the data for insights. A data engineer see to it that out of vast amount of data collected by company, the data is in a highly useable state.
Data Engineers work to build systems that collect, manage and convert raw data into useable information for Data Scientist and Data Analysts. They work include:
- Build, test and maintain data pipeline architectures
- Acquire datasets that align with company or organization needs.
- Collaborate with the company management team to understand organization objectives
- Craft new data validation methods and business/data analysis tools
- Ensures compliance with the ISO data governance and security policies
A career while listed by Dice Insights as the top trending job in the technology industry in 2019, it sometime can seem both rewarding and challenging. A career roadmap in Data Engineering would involve skills in:
- Coding proficiency in SQL, NoSQL, Python, Java, Scala and R.
- Relation and non-relation databases
- ETL Systems such as Xplenty, Stitch, Alooma and Talend
- Data Storage for different data type such as Data Lakes, Data Warehouse.
- Automation and Scripting especially on working with large amounts of data.
- Cloud computing hands on skills and knowledge
Being a dynamic and challenging field that forms the backbone of data-driven organizations and companies that make decision based on data, understanding the core concepts, mastering most essential tools and fulfilling required responsibilities could just make it enough to build a successful entry level career.
To pursue a career in data engineering one may consider complete course work from reupdated course providers such as Coursera IBM Data Engineering, Microsoft azure Data Engineer, Meta Database Engineer, Pluralsight Data Engineering with AWS Machine, Springboard Data Engineering
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