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Interswitch Limited is an integrated payment and transaction processing company that provides technology integration, advisory services, transaction processing ...
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Interswitch Limited is an integrated payment and transaction processing company that provides technology integration, advisory services, transaction processing ...
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Interswitch Limited is an integrated payment and transaction processing company that provides technology integration, advisory services, transaction processing ...
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Data engineers are responsible for designing, maintaining, and optimizing data infrastructure for data collection, management, transformation, and access. They are in charge of creating pipelines that convert raw data into usable formats for data scientists and other data consumers to utilize. The data engineer role evolved to handle the core data aspects of software engineering and data science; they use software engineering principles to develop algorithms that automate the data flow process. They also collaborate with data scientists to build machine learning and analytics infrastructure from testing to deployment. Data engineers help organizations structure and access their data with the speed and scalability they need and provide the infrastructure to enable teams to deliver great insights and analytics from that data.
Work on Data Architecture: They use a systematic approach to plan, create, and maintain data architectures while also keeping them aligned with business requirements.
Collect Data: Before initiating any work on the database, they have to obtain data from the right sources. After formulating a set of dataset processes, data engineers store optimized data.
Conduct Research: Data engineers conduct research in the industry to address any issues that can arise while tackling a business problem.
Improve Skills: They must keep themselves up-to-date with machine learning and its algorithms like the random forest, decision tree, k-means, and others. They are proficient in analytics tools like Tableau, Knime, and Apache Spark. They use these tools to generate valuable business insights for all types of industries. For instance, data engineers can make a difference in the health industry and identify patterns in patient behaviour to improve diagnosis and treatment. Similarly, law enforcement engineers can observe changes in crime rates.
Create Models and Identify Patterns: Data engineers use a descriptive data model for data aggregation to extract historical insights. They also make predictive models where they apply forecasting techniques to learn about the future with actionable insights. Likewise, they utilize a prescriptive model, allowing users to take advantage of recommendations for different outcomes. A considerable chunk of a data engineer’s time is spent on identifying hidden patterns from stored data.
Automate Tasks: Data engineers dive into data and pinpoint tasks where manual participation can be eliminated with automation.
Big data architect
Solutions Architect
Machine learning architect
Technical Architect
Data warehouse developer
Business intelligence developer
SQL: SQL serves as the fundamental skill set for data engineers. One cannot manage an RDBMS (relational database management system) without mastering SQL.
Data Warehousing: Get a grasp of building and working with a data warehouse; it is an essential skill. Data warehousing assists data engineers in aggregating unstructured data, collected from multiple sources. It is then compared and assessed to improve the efficiency of business operations.
Data Architecture: Data engineers must have the required knowledge to build complex database systems for businesses. It is associated with those operations that are used to tackle data in motion, data at rest, datasets, and the relationship between data-dependent processes and applications.
Coding: To link your database and work with all types of applications – web, mobile, desktop, IoT – you must improve your programming skills. For this purpose, learn an enterprise language like Java or C#. The former is useful in open-source tech stacks, while the latter can help you with data engineering in a Microsoft-based stack. However, the most necessary ones are Python and R. An advanced level of Python knowledge is beneficial in a variety of data-related operations.
Operating System: You need to become well-versed in operating systems like UNIX, Linux, Solaris, and Windows.
Apache Hadoop-Based Analytics: Apache Hadoop is an open-source platform that is used to compute distributed processing and storage against datasets. They assist in a wide range of operations, such as data processing, access, storage, governance, security, and operations. With Hadoop, HBase, and MapReduce, you can further your skill sets.
Machine Learning: Machine learning is mostly linked to data science. However, if you can have some idea of how data can be used for statistical analysis and data modelling, it will serve you well during your job as a data engineer.
