Build data pipelines: Managed data pipelines consist of a series of stages through which data flows (for example, from data sources or endpoints of acquisition to integration to consumption for specific use cases). These data pipelines have to be created, maintained and optimized as workloads move from development to production for specific use cases. Architecting, creating and maintaining data pipelines will be the primary responsibility of the data engineer.
Drive Automation through effective metadata management: The data engineer will be responsible for using innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. The data engineer will also need to assist with renovating the data management infrastructure to drive automation in data integration and management.
This will include (but not be limited to):
Learning and using modern data preparation, integration and AI-enabled metadata management tools and techniques.
Tracking data consumption patterns.
Performing intelligent sampling and caching.
Monitoring schema changes.
Recommending ? or sometimes even automating ? existing and future integration flows.
Collaborate across departments: The newly hired data engineer will need strong collaboration skills in order to work with varied stakeholders within the organization. In particular, the data engineer will work in close relationship with data science teams and with business (data) analysts in refining their data requirements for various data and analytics initiatives and their data consumption requirements.
Educate and train: The data engineer should be curious and knowledgeable about new data initiatives and how to address them. This includes applying their data and/or domain understanding in addressing new data requirements. They will also be responsible for proposing appropriate (and innovative) data ingestion, preparation, integration and operationalization techniques in optimally addressing these data requirements. The data engineer will be required to train counterparts such as [data scientists, data analysts, LOB users or any data consumers] in these data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases.
Participate in ensuring compliance and governance during data use: It will be the responsibility of the data engineer to ensure that the data users and consumers use the data provisioned to them responsibly through data governance and compliance initiatives. Data engineers should work with data governance teams (and information stewards within these teams) and participate in vetting and promoting content created in the business and by data scientists to the curated data catalog for governed reuse.
Become a data and analytics evangelist: The data engineer will be considered a blend of data and analytics ?evangelist,? ?data guru? and ?fixer.? This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals.
