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Data Engineer (m/f/d).
A mid-level competency profile.
Companies have been eager to hire data engineers. How would you stand for organizing the infrastructure in a manner that serves the business?
He is a Data Engineer.
He crossed over from a Computational Ph.D. to data engineering, using his skill set to identify use cases suitable for productionization. The exciting combination, in this case, is the STEM Ph.D., the computational skills, the data experience, and the ML skills.
For companies and practitioners: The competency profile validates your technical competency and domain expertise in data. It recognizes you as a specialist and advances your career to Senior, Lead, and Director. By practitioners, for practitioners — this service is provided by the AI Guild.
What do you see in the competency profile?
This data engineering profile shows breadth highly relevant to business decision-making. Data Engineering is the focus of all tasks, driving forward a company builder. The particular strength is
- Engineering solutions for data analytics with Python and SQL
- Visualizing patterns for business decisions
Complementary skills lie in infrastructure management (supporting analytics) and machine learning (supporting use case selection).
Identifying use cases for production
The experienced practitioner may infer that the profile indicates a smaller team, hence the broader range of tasks.
Yet, I think the profile shows focus. For all companies, especially startups, the most important task is identifying use cases suitable for machine learning and then choosing to productionize.
Business orientation
Business-oriented data engineering is a promising career path. It is more than just data engineering with a focus on data and pipelines because its practice is driven by business development, revenue, and cost considerations. I expect Promit to proceed on an accelerated career path.
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Do you want to progress to Senior, Lead, and Director?
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