Role and Responsibility of a Data Engineer

1. Develop ETL (Extract, Transform, Load) processes to help extract and manipulate data from multiple data sources into a single repository (ie., Data Warehouse). Common ETL tools include Xplenty, Stitch, Alooma and Talend.
2. Prepare raw data in Data Warehouses into a consumable dataset for both technical and non-technical stakeholders.
3 Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading of data from a wide variety of data sources.
4. Data cleaning. Generally, the likelihood of errors in data increases with the number of data sources required by a company for its activities. As a result, it s not surprising that data engineers spend most of their time cleaning data that is corrupted, incorrectly formatted, duplicate, or incomplete data.
5. Automate data workflows such as data ingestion, aggregation, and ETL processing.
6. Partner with data scientists and functional leaders in sales, marketing, and product to deploy machine learning models in production.
7. Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership.
8. Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, Azure, GCP).
9. Data monitoring. Between the conception phase and the production phase of a machine learning model, there is a long way full of potential obstacles. Data engineers are also tasked with the monitoring and optimization of the data architecture and data processing systems.
10. Design, build and maintain batch or real-time data pipelines in production.
11. Managing Big Data with tools such as Hadoop, kafka, MongoDB, etc.,

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