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#Dataengineering Reel by @muskan.khannaa - Switching from Testing to Data Engineering felt scary when I had no real experience. My experience was growing but not in the direction I wanted. The
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MU
@muskan.khannaa
Switching from Testing to Data Engineering felt scary when I had no real experience. My experience was growing but not in the direction I wanted. The longer I stayed, the more testing experience I was accumulating! And that scared me! The biggest question I had was: HOW do you justify a switch into Data Engineering when you haven’t worked on it professionally? What worked for me was bridging the gap by showing the work. - I didn’t wait for my job to give me hands-on data work (I might haven’t gotten any tbh) - I built projects that reflected what Data Engineers actually do or atleast come close to it in someway. Those projects did two things: * helped my resume get shortlisted with the right key words and skills added to it * showed interviewers that I genuinely wanted to work in data. And interviewers DO APPRECIATE IT! If you haven’t gotten hands-on exposure at work, personal projects absolutely count. That’s how I made the switch and it worked! If you’re in testing or a service-based role and feeling stuck, switching roles is possible. I’ll break down what kind of projects to pick and how to position them in the next post! . . . . . . [switching from testing to data engineering, testing to data engineer, career transition, service based company to product based company, accenture to data engineering, data engineer projects, working professional, learning data engineering while working, data engineer roadmap, microsoft data engineer] #dataengineering #dataengineer #testing
#Dataengineering Reel by @0umniah (verified account) - Prepping for a Data Engineering interview? I got you! Kheli lya "Data" f commentaire, and I'll send you a cheatsheet with key concepts you need to kno
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@0umniah
Prepping for a Data Engineering interview? I got you! Kheli lya “Data” f commentaire, and I’ll send you a cheatsheet with key concepts you need to know! 😊 🏷️ DataEngineering, TechCareer, Coding, Data, Job, Python, Interviews
#Dataengineering Reel by @sahilgogna_ - This is how you can become top 1% of data engineering candidates! . . . #datascience #dataengineering #python #sql
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@sahilgogna_
This is how you can become top 1% of data engineering candidates! . . . #datascience #dataengineering #python #sql
#Dataengineering Reel by @radha.shrivastavaa (verified account) - ✅Save & Share this reel with your DATA ENGINEER aspiring friends✅ 👇Ask any queries you have related to data engineering in comments 👇 👉🏻Roles &
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@radha.shrivastavaa
✅Save & Share this reel with your DATA ENGINEER aspiring friends✅ 👇Ask any queries you have related to data engineering in comments 👇 👉🏻Roles & Responsibilities of Data Engineerings- collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. 👉🏻Qualification for Data Engineering- Degree in Computer Engineering, Mathematics, or other engineering disciplines. (PS with time degrees aren’t limited to some profession) 👉🏻Skills Required for Data Engineering- Data engineers use tools such as Python, Spark, Kafka, SQL, Tableau, Snowflake, etc., for various big data activities such as data analytics, data processing, etc. 👉🏻 Salary of Data Engineers- an average annual salary of ₹ 7.2 Lakhs. . . . . . . . #dataengineer #dataanalytics #dataengineering #dataentryjobs #datascience #datascientist #datavisualization #dataprotection #datarecovery
#Dataengineering Reel by @data_engineer_academy - hey guys, if you're new to our page, my name is Christopher Garzon. I run the top Data Engineering Academy in the country, where we help students tran
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@data_engineer_academy
hey guys, if you’re new to our page, my name is Christopher Garzon. I run the top Data Engineering Academy in the country, where we help students transition into data engineering from other data professions to increase their compensation. How I got here… At 18 years old, I started at Boston College. At 20, I was sneaking into graduate-level classes to take machine learning and data science courses. At 21, I invested in a data science course from a mentor and wired him $3,000 without ever meeting him. At 22, I landed my first job as a data analyst at Amazon, making $60,000 per year. At 24, I became a data engineer at Amazon, increasing my salary to $100,000 and started angel investing in a couple of data companies. At 25, I moved to a startup as a data engineer and doubled my income to $200,000 per year. At 26, I was making about $350,000 at Lyft. At 27, Lyft stocks went up, and my total compensation reached around $450,000. That same year, I launched the Data Engineering Academy. For the last two and a half years, I’ve been running the Data Engineering Academy full-time, helping thousands of people transition into data engineering and significantly increase their earning potential. If you’re looking to get 1-1 help from me and my team to transition into data engineering, comment “JOIN.”
#Dataengineering Reel by @ndtvprofit (verified account) - Over two decades of experience, Total Environment has completed dozens of projects in Bangalore, Hyderabad, and Pune, totalling more than 4 million sq
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@ndtvprofit
Over two decades of experience, Total Environment has completed dozens of projects in Bangalore, Hyderabad, and Pune, totalling more than 4 million square feet for over 1,200 customers. While these figures show the scale of operations and ability to influence modern housing. What truly moves and inspires them is passion for design and the satisfaction of giving people a place to call home & on the other side of the break is Algoleap, a digital engineering and technology consulting firm that focuses on application modernization, digital platform engineering, product development, and data engineering, Algoleap's growing team of top engineering talent delivering critical capabilities through design thinking lead enterprise architecture, best in class engineering practices, and managed innovation, and at the heart of the business are their people and customers. For the latest news and updates, visit http://ndtvprofit.com
#Dataengineering Reel by @guviofficial (verified account) - Start your career in Data engineering and get placement support with GUVI by just clicking on the link in bio📢 #Data #engineering #datascience #guvi
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@guviofficial
Start your career in Data engineering and get placement support with GUVI by just clicking on the link in bio📢 #Data #engineering #datascience #guvi #coding #techcourses
#Dataengineering Reel by @meet_kanth (verified account) - Data Engineering Textbooks [Free Download Link] Comment "PDF" to get the link #python #sql #free #textbooks #dataengineer
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@meet_kanth
Data Engineering Textbooks [Free Download Link] Comment "PDF" to get the link #python #sql #free #textbooks #dataengineer
#Dataengineering Reel by @govtechsg (verified account) - Don't talk to us until you've watched our latest GovTech Decoded episode! Get the inside scoop on GovTech's Data Engineering Practice team and why th
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@govtechsg
Don’t talk to us until you’ve watched our latest GovTech Decoded episode! Get the inside scoop on GovTech’s Data Engineering Practice team and why their tech and AI solutions matter on the latest episode of #GovTechDecoded: go.gov.sg/govtechdecoded-ep7 #OfficeHumour #TechForPublicGood #GovTechSG
#Dataengineering Reel by @nataindata (verified account) - 🫨🙄We occasionally get pissed at: 1. Business folks who attempt do everything in Excel. 2. HR people who think Data Engineering is Data Science. 3.
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@nataindata
🫨🙄We occasionally get pissed at: 1. Business folks who attempt do everything in Excel. 2. HR people who think Data Engineering is Data Science. 3. Data Scientists who hate and look down on data preparation, cleansing, storing, and exposing (read: Data Engineer tasks) 4. Project Managers who attempt to measure contribution in terms of “lines of code written” or “commits” or some other nonsense Software Engineering metric. 5. HR people who think Data Engineering is Software Engineering. 6. Primary Keys. Especially composite ones. 7. Data dictionaries. Especially if they don’t exist. 8. Updates vs. Appends. 9. Some management trainee, who believes issuing everyone an expensive Macbook is supposed to solve many things. (Keynote and Photoshop aren’t ETL tools brother.) 10. HR people who think data engineers are full-stack developers. 11. Anyone who says Data Engineering is just database management. 12. Query jobs that start with “SELECT * FROM Table”. 13. Indexes. Especially if there are none. Most especially if there are too many. 14. Wide tables, especially if they get wider. 15. Sub-queries, joins and views. Especially if they are abused for each other. 16. Data Scientists, who insist on extracting all the data into their Python notebooks. Especially when the data won’t fit in their laptop memory 17. Everyone who thinks data engineers can fix printers, photocopiers, air conditioners and replace light fixtures. 18. Bulk loads vs. Inserts. 19. Database logs. Especially if they don’t exist. 20. Cloud providers. Especially their billing systems. Agree? 🫠😅 #programmingmemes #codingmemes #programmingjokes #programminghumor #codinghumor #memes #data #datascience #datascientist #dataengineer #sql #programmer #programminglife
#Dataengineering Reel by @penelope_data - 9 data projects ideas instead of doomscrolling: (And there is a repo with the data :) ) 🤖 Machine Learning Projects * Diabetes Classification
Build
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@penelope_data
9 data projects ideas instead of doomscrolling: (And there is a repo with the data :) ) 🤖 Machine Learning Projects * Diabetes Classification
Build and compare classification models to show how data preprocessing, feature scaling, and hyperparameter tuning directly improve predictive performance. * Heart Attack Prediction
Implement an end-to-end classification pipeline—from raw data to model evaluation—to demonstrate a realistic machine learning workflow. * Medical Cost Prediction
Train a regression model to predict healthcare costs, emphasizing feature importance analysis and model optimization to explain what drives predictions. 🛠️ Data Engineering Projects * NBA Player Statistics ETL Pipeline
Design an ETL pipeline that extracts player statistics, cleans and transforms the data, and stores it in a relational database for reliable downstream analysis. * Real-Time & Batch Data Pipelines with Kafka
Build a scalable pipeline that processes streaming and batch data using Kafka, PostgreSQL, and Docker to demonstrate modern data flow architecture. * Glassdoor Job Data Pipeline
Scrape job postings, clean and structure the raw data, and prepare it for analysis and visualization to showcase real-world data ingestion challenges. 📊 Data Analytics Projects * Pokémon Dataset Analysis
Perform exploratory data analysis and feature engineering to uncover patterns in Pokémon characteristics such as types, stats, and legendary status. * Automated EDA Tool Comparison
Benchmark AutoViz, SweetViz, and Pandas Profiling across multiple datasets to evaluate performance, resource usage, and practical trade-offs. * Exploratory Job Market Analysis
Analyze cleaned job posting data to extract trends, key skills, and role distributions using visualizations and summary statistics. 👉🏻 Comment « data » to get the link to the repo and portfolio strategies! #data #students #job
#Dataengineering Reel by @awsdevelopers (verified account) - When you work in tech, the learning never stops. 🎓 From data engineering to scalable architectures & Amazon Bedrock agents, hear how AWS Heroes are
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@awsdevelopers
When you work in tech, the learning never stops. 🎓 From data engineering to scalable architectures & Amazon Bedrock agents, hear how AWS Heroes are sharpening their tech skills. What topic or area of tech has caught your interest lately? Let us know what you’re learning about in the comments. 👇 To hear more from our AWS Heroes, follow @awsdevelopers. #AWS #Tech #Learning #AmazonBedrock

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