I will be using a scale of 10 for better quantification.
Python : 7/10 SQL: 7/10 PowerBI: 9/10 ML(Pandas): 6/10 PLSQL: 6/10 Advanced Excel: 8/10 Azure Databricks + DataFactory : Started learning Apache Spark: Started learning Communication skills: 9/10
Prior Work Ex: 1.8yrs as a PLSQL developer (started off as a fresher) + one SAP internship of 4months + one Data analyst internship of 1 month
Aspiration : Data Engineering Role
Is the above techstack enough to get me a good data engineering role? Will I be able to clear the technical rounds at companies like accenture, IBM etc with this profile? I have heard the barrier to entry for data engineering is higher than that of data analyst.
Do I need to learn DSA?
What salary range do I fall in with this profile?
I seek your honest answer. I feel lost.
Also, should I focus more on improving my cloud skills or shall I be more competent with my python skills? Which one would pay me off in a better sense financially? I certainly can not focus on all the tools at once.
This tech stack is enough for DE roles. DSA is generally asked these days. Concentrate on easy/mid level leetcode.
Practice SQL advanced, ETL concepts. Designing schema’s. It should be more than enough.
@TheOatmeal Okay,
The thing is I am highly intimidated by DSA. You know the kind of hype that these youtubers have made, I just feel so uncomfortable when I look at those questions. This is primarily why I never got inclined to roles such as SDE that require vanilla coding. Any way to loosen that up? How do I get rid of that?
You won’t be needing that level dsa. General Array, list, Linked List types questions would be enough.
Also its not necessary that everyone will ask these questions. Most of them are SQL heavy.
**CORRECTION" ML as in Scikit, Pandas, algorithm working knowledge etc
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Skillset : SQL, PowerBI, Python, Advance Excel, ML, Very basic Azure (Just started learning) 1.5 yrs of experience in TCS in PLSQL DB Team
I am confused as to which field should I venture into,
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