#Data Engineer

After 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

Comment roadmap to get sent my free and complete data engineering roadmap!

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

🚀 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]

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?

How I’d become a Data Analyst in 2026 ⬇️ 1️⃣ Get in the door (any role) Data Analyst titles are hard to land, degree or not. So get into any role at a tech forward company with an analytics team/department . Sales. Ops. Data entry. Work up! Prove your value. That’s exactly what I did. 2️⃣ Improve what’s in front of you Look for small things you can control: • Excel • MS Access • Power Query Invoices research (ms access), trends, reports doesn’t matter, anything YOU can do. 3️⃣ Learn only what you need Target the tools you’re already working with/access too. (DataCamp and Codecademy worked for me) 4️⃣ Build something real Not tutorials. Build a tool people (and you) actually use even if it’s simple. Examples could be: Using forms and VBA/SQL in ms access to build a form for people to researching invoices! 5️⃣ Show your work Demo it. Explain the impact. Who uses it. Why it matters. And how it helps! 6️⃣ Say yes to opportunities Take on EVERYTHING, prove you can do the work, even if it adds more stress. That’s how you stack proof for the next role. No degree required. 👉 Follow if you’re breaking into data. #dataanalyst #howto #breakintotech #nodegree #2026goals

Want to become a Data Engineer? This is the complete Data Engineer roadmap you need. Comment “Data” to get Detailed Guide 📩 Follow @amanrahangdale_2108 for more Roadmaps, Coding, AI, and Career Tips every Day

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

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

A lot of you guys asked me what all things are needed to become a Data Engineer. So here it goes 👇 1. SQL This is the backbone of data engineering. You should be comfortable with joins, window functions, CTEs, optimization, and handling large datasets. 2. Python From data processing to automation, Python is everywhere in data engineering. Focus on writing clean logic and understanding libraries like Pandas and PySpark. 3. Data Engineering Concepts This is where most people struggle. Learn things like: ETL/ELT pipelines Data Warehousing Batch vs Streaming Spark fundamentals Airflow orchestration Cloud platforms Data modeling 4. Projects The best way to learn is by building. Create pipelines, dashboards, end-to-end projects, and deploy them. 5. Communication & Problem Solving In real jobs, understanding business problems matters as much as writing code. And most importantly: You do NOT need to learn everything at once. Start small. Stay consistent. Data Engineering is one of the best career paths right now if you genuinely enjoy building things with data. {Data engineering, data engineer, data analyst, amazon data engineer, sql} #explorepage #explore #fypppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp #page #dataengineering

On 1st January 2020, I had set an audacious target of getting into Data engineer role in three months despite having 0 skillsets for it. And on this 13th March , exactly 72 days later , I received first of my 5 offers for Data Engineer. And this didn’t happen co-incidentally. It happened due to a structured plan and dedicated 3 hours of work that I put every day on weekdays after office and atleast 10 hours work on weekends teaching myself SQL, Python, Data Design , technologies like Spark, Kafka, Kubernetes, Docker and so on. And this is the day by day 90 day plan that I had followed. Now, this plan requires you to work hard, it requires you to be consistent but if you follow it diligently, its 99% likely to land you multiple interviews and your dream data engineer job by 21st July 2026 .(90 days from today) Follow and comment “DATA” to get it . . . #data #dataengineer #jobs

Comment “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!