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๐Ÿ’ฌ
Striving hard
๐Ÿ’ฌ
Striving hard
  • Northeastern University
  • Boston, MA
  • 07:31 (UTC -12:00)

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Ynpreet/README.md

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Hi, I'm Preet

Manager - Healthcare Analytics | Data Engineer ๐Ÿš€


๐Ÿš€ About Me

  • ๐Ÿ“Š Manager, Healthcare Analytics at EXL Services.
  • ๐Ÿ’ผ Previously worked at CVS Health, LinkedIn, and Google Partners.
  • ๐ŸŽ“ Certified Google Cloud Professional Data Engineer, Tableau Desktop Specialist, and LookML Developer.
  • ๐Ÿ’ก Passionate about data modeling, ETL pipelines, and cloud platforms.
  • ๐ŸŒฑ Currently working on Databricks, Azure Data Factory, and PySpark.

๐Ÿ“Š My GitHub Stats


๐Ÿ› ๏ธ Tools and Technologies

Python MySQL Apache Docker Azure


+@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @+
@@       o o                                           @@
@@       | |                                           @@
@@      _L_L_                                          @@
@@   โฎ\/__-__\/โฏ Programming isn't about what you know @@
@@   โฎ(|~o.o~|)โฏ  It's about what you can figure out   @@
@@   โฎ/ \`-'/ \โฏ                                       @@
@@     _/`U'\_                                         @@
@@    ( .   . )     .----------------------------.     @@
@@   / /     \ \    | while( ! (succeed=try() ) ) |     @@
@@   \ |  ,  | /    '----------------------------'     @@
@@    \|=====|/                                        @@
@@     |_.^._|                                         @@
@@     | |"| |                                         @@
@@     ( ) ( )   Testing leads to failure              @@
@@     |_| |_|   and failure leads to understanding    @@
@@ _.-' _j L_ '-._                                     @@
@@(___.'     '.___)                                    @@
+@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @+

๐™ถ๐š’๐š๐š‘๐šž๐š‹ ๐™ฒ๐š˜๐š—๐š๐š›๐š’๐š‹๐šž๐š๐š’๐š˜๐š— ๐™ถ๐š›๐šŠ๐š™๐š‘



๐Ÿ“ž Connect With Me

LinkedIn Instagram


๐Ÿ”ฅ Fun Facts

  • By 2025, Indiaโ€™s analytics industry is projected to grow eightfold, reaching $16 billion.
  • Less than 0.5% of data created globally is analyzed or used effectively.
  • Data engineering powers over 80% of AI models, making it the backbone of machine learning.

๐Ÿ’ป Happy Coding!

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