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#Data Engineer Reel by @muskan.khannaa - You DO NOT need to learn everything to become a Data Engineer.
People often prepare for mid-level roles while applying for entry-level roles.

Here's
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@muskan.khannaa
You DO NOT need to learn everything to become a Data Engineer. People often prepare for mid-level roles while applying for entry-level roles. Here’s what actually mattered for me in the beginning when switching from testing to a data engineer role. 1. SQL(non-negotiable): You’ll need to know the basics and complexities of sql along including subqueries and window functions. If you’re not strong in SQL, you won’t be able to move forward in interviews. 2. Python concepts basics like lists, dictionaries, sets and basic problem solving. You can solve questions in other languages too but I’d suggest Python as it’s easy to learn. You don’t need hardcore DSA for most entry-level Data Engineering roles, but DSA is definitely important. 3. Data warehousing concepts like facts vs dimension, star vs snowflake schema, SCD Type 1,2 etc. Understanding concepts and what data warehousing is and why it’s there mattered more than tools. 4. ETL and data pipeline understanding. How data is extracted, transformed, loaded is the CORE of Data Engineering. You don’t need spark understanding in the beginning, just the understanding of how data flows in and out. 5. System design basics, not like design twitter/uber. Simple understanding of how data moves end to end and overall understanding of data eco-systems. No deep design is expected at entry-level. 6. Pick any one cloud. Don’t chase all clouds, just any one cloud and cover its basics because you’d most likely be working on some cloud in your work. I moved from Testing to Data Engineering by focusing on these basics, instead of trying to learn every other tool out there, and it is still the very core of Data Engineering which one must know to crack interviews. Save this if you’re planning to make a switch into Data Engineering. . . . . . [data engineering roadmap, entry level data engineer preparation, switching to data engineering, testing to data engineering, data engineer interview preparation, sql for data engineering, python basics for data engineer, data engineers for beginners, microsoft data engineer] #dataengineer #dataengineering
#Data Engineer Reel by @hustleuphoney - 🚀 Day 1: Noob to Pro Data Engineer 🚀

Started my journey today! 🔥 Learned about Apache Spark and how it helps solve the 3V problem (Volume, Velocit
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@hustleuphoney
🚀 Day 1: Noob to Pro Data Engineer 🚀 Started my journey today! 🔥 Learned about Apache Spark and how it helps solve the 3V problem (Volume, Velocity, Variety). Also compared Hadoop vs. Spark—turns out Spark is way faster! ⚡ 💡 Key Takeaways: ✅ Spark processes data in-memory, making it much faster than Hadoop. ✅ Hadoop is great for batch processing, but Spark shines in real-time analytics. ✅ Practiced SQL on LeetCode & started working on my Azure Data Engineering project. [Azure, cloud, learn, study, hardwork, consistency, hustle, motivation, job, employment, Microsoft azure, hadoop, dpark, daily vlog, daily study, unemployment, mnc, jio, corporate]
#Data Engineer Reel by @the.datascience.gal (verified account) - Data Engineer vs AI Engineer.
Here's what each role does, what they earn, and how to choose.

What You Actually Do:

Data Engineer: Pipelines and reli
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@the.datascience.gal
Data Engineer vs AI Engineer. Here’s what each role does, what they earn, and how to choose. What You Actually Do: Data Engineer: Pipelines and reliability. Ingest, transform, model, validate. If data breaks, everything downstream breaks. Building data foundations that analytics, ML, and product teams rely on. AI Engineer: Models in production. RAG systems, agent evaluations. If the model is slow, wrong, or unsafe, you fix it. Building AI features like chat, search, copilot, automations that users actually touch. Languages You Use: Data Engineer: SQL all day, Python for pipelines, Scala or Java for Spark. AI Engineer: Python for model workflows, TypeScript or JavaScript for APIs, some SQL. Tech Stack: Data Engineer: Snowflake, BigQuery, Redshift, dbt, Airflow, Kafka, Databricks, Spark, Monte Carlo. AI Engineer: OpenAI, Anthropic, Gemini, LangChain, LangGraph, Pinecone, Weaviate, Fireworks AI, Ragas, LangSmith, Weights & Biases. Salary Ranges (NYC/SF): Data Engineer: $140K-$200K base, $170K-$240K total comp AI Engineer: $160K-$230K base, $200K-$300K total comp (higher at AI-first companies with equity) Interested in data and building scalable systems? Data engineering. Like AI and want to work with models in production? AI engineering.​​​​​​​​​​​​​​​​
#Data Engineer Reel by @mrk_talkstech (verified account) - Data Engineers work tirelessly behind the scenes to build the infrastructure for data projects. However, their efforts often remain invisible to busin
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@mrk_talkstech
Data Engineers work tirelessly behind the scenes to build the infrastructure for data projects. However, their efforts often remain invisible to business users, who focus on the end product and reward Data Scientists and Analysts with more recognition! #dataengineering #azure #pyspark #dataengineer #azuredataengineer #data #aws #gcp #azuredatabricks #dataanalyst #datascientist #datascience
#Data Engineer Reel by @sundaskhalidd (verified account) - It's great career but here are 3 reasons to not become a data engineer. Do you agree?
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#dataengineer #techcareer #breakintotech
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@sundaskhalidd
It's great career but here are 3 reasons to not become a data engineer. Do you agree? ㅤ #dataengineer #techcareer #breakintotech
#Data Engineer Reel by @vee_daily19 - DATA ENG - 90 day prep resources 
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{data engineering , resource , tech ,projects, internships, job search }
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#technology #trending #jobsear
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@vee_daily19
DATA ENG - 90 day prep resources . . . {data engineering , resource , tech ,projects, internships, job search } . . #technology #trending #jobsearch #parttime #techconsulting #tech #hacks #behavioral #nodaysoff #veeconsistent #linkedin #emails #dataengineering
#Data Engineer Reel by @eczachly (verified account) - Comment roadmap to get sent my free and complete data engineering roadmap!
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@eczachly
Comment roadmap to get sent my free and complete data engineering roadmap!
#Data Engineer Reel by @onseventhsky (verified account) - A day as data analyst
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#fypage #explorepage✨ #viral #trending #vlog #office #wfo #gwrm #bollywood #ethnic #desi #corporate  #o
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@onseventhsky
A day as data analyst . . . . . . . . . . . . #fypage #explorepage✨ #viral #trending #vlog #office #wfo #gwrm #bollywood #ethnic #desi #corporate #ootd #fashiongram #data #analyst
#Data Engineer Reel by @sdw.online (verified account) - Comment 'Link' below if you want a free guide on how I got my first data analyst role ✨

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@sdw.online
Comment 'Link' below if you want a free guide on how I got my first data analyst role ✨ -------------------------------------------------------------- YouTube channels for data engineers ✨ - Seattle Data Guy - Data with Zach - Andreas Kretz - Gowtham (Data Engineering) Who else belongs on this list?
#Data Engineer Reel by @arshgoyalyt (verified account) - 6 Months No BS Roadmap to become a Data Engineer and get a job from scratch.

1. Master Python and SQL - focus on Pandas, NumPy, and write complex SQL
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@arshgoyalyt
6 Months No BS Roadmap to become a Data Engineer and get a job from scratch. 1. Master Python and SQL - focus on Pandas, NumPy, and write complex SQL queries. Learn from W3Schools, FreeCodeCamp (English) or CodeWithHarry (Hindi). Practice on LeetCode SQL and HackerRank. 2. Learning databases and data warehousing. Understand OLTP vs OLAP systems. Get hands-on with PostgreSQL, learn Snowflake or BigQuery basics. Study data modeling from DataCamp or Simplilearn tutorials (both Hindi/English available). 3. ETL/ELT tools and orchestration. Start with Apache Airflow - watch tutorials on YouTube from Coder2Hacker (Hindi) or TechWithTim (English). Build a simple pipeline that moves data from an API to a database. 4. Big data and cloud platforms. Learn Spark basics from PySpark tutorials. Pick AWS, GCP, or Azure - follow their free tier documentation. Learn Docker and master Git. 5. Build real projects - a streaming pipeline, a data warehouse setup, ETL automation. Publish on GitHub, share on LinkedIn, and apply for jobs. If you want to know which companies hire for Data Engineers and a detailed guide on that, comment below and stay tuned for next part. #dataengineer #roadmap #data #cloud #azure
#Data Engineer Reel by @push__to__prod (verified account) - Do you think switching too often hurts the resume 
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( Dsa, career upgrade, switching strategy, tech careers India, Bangalore salaries, 9 to
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@push__to__prod
Do you think switching too often hurts the resume . . . . . . ( Dsa, career upgrade, switching strategy, tech careers India, Bangalore salaries, 9 to 5, high paying jobs, Data engineer) #tcs #motivation #career #growth #salary
#Data Engineer Reel by @maggieindata (verified account) - Comment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊

A good n
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@maggieindata
Comment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊 A good number of portfolio projects is 3–5, and the types of projects you choose should reflect the kind of data role you’re going after. A data analyst portfolio should look very different from a machine learning engineer one. Even within data science, a product/decision data scientist portfolio should focus on A/B testing and metrics storytelling—while an algorithm data scientist portfolio might highlight modeling and experimentation. ✨ Especially if you’re building your very first project, prioritize: 🌱 Real-world messiness (not polished Kaggle sets) 🌱 Business context and decision-making 🌱 Clear documentation (what you did and why) 🌱Visuals to help your work stand out No one’s asking for perfection—they want to see how you think. #datascienceportfolio #dataanalyst #learnpython #codingjourney #techcareers

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