223.6K
ECComment roadmap to get sent my free and complete data engineering roadmap!
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TEAfter working as a data engineer, here are 5 things I wish I knew earlier:
1. It’s not just SQL or Python
Data engineering isn’t about syntax
It’s about moving data reliably between systems and transforming it correctly along the way
2. Testing data is surprisingly hard
Testing backend code is straightforward → input vs expected output
In data engineering → massive datasets, multiple columns, edge cases… validating correctness is a real challenge
3. It gets harder as you grow
Junior role → write SQL / PySpark pipelines.
Senior role → design architecture, ensure data governance, manage scalability, reliability, and costs.
4. “Pipelines once built are done” — wrong
Data pipelines break. Schemas change. Upstream systems fail.
Maintenance and monitoring are ongoing responsibilities, not one-time work.
5. “More tools = better engineer” — myth
Knowing 10 tools doesn’t matter.
Understanding fundamentals (data modeling, distributed systems, trade-offs) is what actually scales your career.
If you focus only on coding, you’ll plateau early.
If you understand data systems, you’ll grow fast.
💾 Save this for when the role starts feeling more complex than expected
💬 Comment if you’ve felt this shift already
🔁 Follow to keep your thinking sharp as you grow in data engineering
@techwithprateek
173.1K
HU🚀 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]
@hustleuphoney
306.7K
VEDATA ENG - 90 day prep resources
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{data engineering , resource , tech ,projects, internships, job search }
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#technology #trending #jobsearch #parttime #techconsulting #tech #hacks #behavioral #nodaysoff #veeconsistent #linkedin #emails #dataengineering
@vee_daily19
39.0K
MRData 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
@mrk_talkstech
63.0K
THData 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.
@the.datascience.gal
122.3K
MAComment 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
@maggieindata
7.8K
SDComment 'Link' below if you want a free guide on how I got my first data analyst role ✨
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YouTube channels for data engineers ✨
- Seattle Data Guy
- Data with Zach
- Andreas Kretz
- Gowtham (Data Engineering)
Who else belongs on this list?
@sdw.online
111.1K
CHHere is a full roadmap on how to get started with Data Science. Comment “DATA” for the full roadmap pdf.
#datascience #machinelearning #coding #ai #university
@chrispathway
554.4K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
243.3K
JEComment “project” for my full video that breaks each of these projects down in detail with examples from my own work.
If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude!
Here are the 3 types of projects you need:
↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables
↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one.
↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it
Save this video for later + send to a data friend!
@jessramosdata
63.6K
MUThe hardest part of switching to Data Engineering isn’t learning tools, it’s proving experience.
A lot of you asked what kind of projects I had on my resume when I switched from testing to data engineering, without a prior DE background.
Here’s the honest part:
- I didn’t try to build several “perfect” end-to-end projects.
- I picked 2 projects very intentionally.
I wasn’t trying to become an expert, I just tried to show relevant experience in the way I could.
• Projects that showed the flow of data
• Projects that touched multiple layers in actual DE work: ingestion/extraction → transformation → loading → orchestration / dashboards
• Projects I could confidently explain in interviews, even if I hadn’t built every single thing from scratch!
One project helped me show data pipelines + orchestration with Airflow
Another helped me show analysis + dashboards
I also slightly tailored my resume based on the role:
*Not flooding it with tools.
*Not faking things I couldn’t explain.
At that time, I did not take any Data Engineering certifications. I had basic SQL exposure, some ETL (Informatica), a bit of Snowflake and a lot of studying on my own.
The goal was simple:
- get past ATS
- get interview calls
- then learn fast on the job
This was back in 2022.
Things have changed and today, projects need to be curated differently.
A lot of you also asked:
“Is it even possible to switch to Data Engineering after many years in testing?”
Short answer: yes! But the approach changes with experience.
I’ll break this down properly in the next video.
Comment PROJECT if you want a detailed breakdown of:
• how to choose projects today
• how to write them on your resume
• what not to fake
And I will share it in a separate post soon!
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[switching from testing to data engineering, data engineer roadmap, testing to data engineer, etl to data engineer, career transition, learning data engineering while working, microsoft data engineer]
#dataengineering #dataengineer
@muskan.khannaa
✨ #Data Engineering Discovery Guide
Instagram hosts 656K posts under #Data Engineering, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Data Engineering is one of the most engaging trends on Instagram right now. With over 656K posts in this category, creators like @chrisoh.zip, @vee_daily19 and @jessramosdata are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
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