Navigating Data Careers

For those looking to start a career in data engineering or eyeing a career shift, here's a roadmap to essential areas of focus: 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Learn both full and incremental data extraction methods. - 𝗗𝗮𝘁𝗮 𝗟𝗼𝗮𝗱𝗶𝗻𝗴: - 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Master the techniques of insert-only, insert-update, and comprehensive insert-update-delete operations. - 𝗙𝗶𝗹𝗲𝘀: Understand how to replace files or append data within a folder. 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 - 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀: Acquire skills in manipulating CSV and Parquet file data with tools like Pandas and Polars. - 𝗦𝗤𝗟: Enhance your ability to transform data within PostgreSQL databases using SQL. This includes executing complex aggregations with window functions, breaking down transformation logic with Common Table Expressions (CTEs), and applying transformations in open-source databases such as PostgreSQL. 𝗗𝗮𝘁𝗮 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Develop the ability to create a Directed Acyclic Graph (DAG) using Python. - Gain expertise in generating logs for monitoring code execution and incorporate logging into databases like PostgreSQL. Learn to trigger alerts for failed runs. - Familiarize yourself with scheduling Python DAGs using cron expressions. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗞𝗻𝗼𝘄-𝗛𝗼𝘄 - Become proficient in using GIT for code versioning. - Learn to deploy an ETL pipeline (comprising extraction, loading, transformation, and orchestration) to cloud services like AWS. - Understand how to dockerize an application for streamlined deployment to cloud platforms such as AWS Elastic Container Service. 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗮𝗻𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Begin your learning journey here : https://lnkd.in/e5BxAwEu Mastering these foundational elements will equip you with the understanding and skills necessary to adapt to modern data engineering tools (aka the modern data stack) more effortlessly. Congratulations, you're now well-prepared to start interviewing for data engineer positions! While there are undoubtedly more advanced topics to explore such as data modeling , the courses and key areas highlighted above will give you a solid starting point for interviews.

𝗢𝗻𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝗵𝗮𝘁 𝗧𝗮𝘂𝗴𝗵𝘁 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗔𝗻𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗘𝘃𝗲𝗿 𝗗𝗶𝗱 Remember the roadmap for Data Engineering looked like a never-ending list of tools? Learning Data Engineering meant learning 10 tools back-to-back causing chaos. Everywhere we looked, it was: “Master Airflow, Spark, Kafka, DBT, Snowflake, Docker… or you’re not job-ready.” It sounded great on paper, but honestly? We couldn’t explain any of it end-to-end. That's when we decided to stop chasing those checkmarks and pick one project to learn and showcase our experience, from start to finish. We set out to build something simple, but complete: 🎯  YouTube Trending Video Tracker  • 𝗙𝗲𝘁𝗰𝗵 𝗱𝗮𝘁𝗮 𝗳𝗿𝗼𝗺 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗔𝗣𝗜 𝘂𝘀𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 ✅ Runs as a Python script inside an Airflow DAG (on an EC2 machine, Cloud Composer, or local Airflow setup)  • 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗱𝗮𝘁𝗮 𝘂𝘀𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 ✅ Runs in the same Python script, inside Airflow task or a separate Python module  • 𝗟𝗼𝗮𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 ✅ Done by Python using Snowflake Connector — also inside the Airflow DAG  • 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝘁𝗵𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 ✅ Airflow runs on a VM (e.g., AWS EC2, GCP Composer, or local server)  • 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘂𝘀𝗶𝗻𝗴 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 ✅ Streamlit app runs separately — typically on a local machine, Streamlit Cloud, or a web server (e.g., EC2) That’s it. Just one project — but done properly, from start to finish. And guess what? → It gave real confidence → Finally understood the flow of a pipeline - how data moves, transforms, and becomes useful. → Had something solid to talk about in interviews ⚠️ 𝗪𝗵𝗮𝘁 𝗥𝗲𝗲𝗹𝘀 𝗦𝗮𝘆: “Learn 10 tools in 30 days” ✅ 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗪𝗼𝗿𝗸𝘀: 𝗚𝗼 𝗱𝗲𝗲𝗽 𝗶𝗻𝘁𝗼 𝗼𝗻𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — and build everything around it. Freshers, if you’re feeling stuck or overwhelmed, here’s my advice: Don’t learn tools in isolation. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲. 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗲 𝗮 𝗯𝗶𝘁. 𝗗𝗲𝗽𝗹𝗼𝘆 𝗶𝘁. 𝗦𝗵𝗼𝘄 𝗶𝘁. 𝗧𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁. That’s how you stand out. 📌 Here’s a simple architecture diagram below if you’re willing to get started 👇 #data #engineering #reeltorealdata #YouTube #ETL

Want to become a Data Analyst? ... my own realistic roadmap.........based on what actually worked for me When I started, I tried learning everything at once.........Python, SQL, ML, dashboards, and what not. Result? Burnout. Confusion. Imposter syndrome. when i had to start over, here’s exactly what I’d do: ✅ Phase 1: Start simple, build confidence 🎯 Excel – Learn pivot tables, VLOOKUP/XLOOKUP, conditional formatting 🎯 Power BI – Build your first dashboard. Learn DAX basics. 🎯 SQL – SQL queries. (This alone will make you job-ready) 📌 Do 1–2 small projects with just these tools. Focus on storytelling. ✅ Phase 2: Go deeper, get confidence 🎯 Python – Learn pandas for data cleaning, numpy Array and matplotlib & seaborn for visuals. 🎯 Statistics Basics – Central Tendency , Measure of dispersion. 🎯 Data Projects – Clean messy datasets, build dashboards, derive insights. 🔄 Mandatory ( must to have): GitHub, resume-building which is ATS Friendly, LinkedIn Optimization etc. 🤖 But what about Machine Learning? You don’t need it to become a data analyst. But if you’re curious, explore these: 🎯 Linear/Logistic Regression 🎯 Decision Trees & Random Forest Only models you can explain, not just run. 💬 A message from someone who's been in your shoes: I know how overwhelming this path can feel. But the secret isn’t learning 100 tools ........... it’s staying consistent with 3–4. 📌 Save this post. Come back when you feel lost........... And remember: 💡 Depth > Variety. Progress > Perfection. You’ve got this. One step at a time. 👣 follow for more Priyanka SG Data Analyst Mentorship : https://lnkd.in/gasgBQ6k #DataAnalytics #DataAnalystRoadmap #PowerBI #SQL #ExcelToPython #CareerSwitch

I've received a lot of DMs asking roadmap, where to start, what to learn first, how to get confidence and so on. No worry I got your back. Here's the best way to learn about #dataengineering. Let me share my approach with you. 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 B𝐮𝐢𝐥𝐝𝐢𝐧𝐠 F𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧  #SQL is the backbone of data engineering as it is used for querying and managing data in relational databases and most important to ace in Data Field. Focus on mastering the basics such as SELECT, JOIN, GROUP BY, and aggregate functions. Additionally, learn advanced concepts like indexing, query optimization, and window functions. Set a deadline for yourself to become proficient in SQL, and practice regularly using platforms like #HackerRank, #LeetCode, #DataLemur, or real-world datasets. #Python is easy to learn and it's essential for data engineering tasks such as data manipulation, automation, & integration with other tools. Aim to understand the core syntax, data structures, and libraries like #Pandas, #NumPy. While deep knowledge of data structures and algorithms isn't necessary, having a moderate understanding will be beneficial. Focus on writing clean and efficient code. #ApacheSpark is a powerful tool for processing large datasets efficiently. Mastery on it, Understand its internal architecture, including concepts like RDDs, DataFrames, & the execution model. Learn how Spark handles big data through transformations and actions. Explore the Spark ecosystem and practice by building simple ETL pipelines. Familiarize yourself with PySpark to leverage Python’s simplicity in Spark applications. Practice it on local or on #Databricks platform In addition to these, learning cloud platforms is essential. Whether you choose #AWS, #Azure, or #GCP, mastering one will make it easier to learn the others. Start by developing a basic foundation in cloud concepts, then focus on services relevant to data engineering, such as data storage, data pipelines, compute services. Don't try to learn everything at once; select the services you need & build from there. 𝐑𝐞𝐬𝐭 a𝐥𝐥 L𝐞𝐚𝐫𝐧 𝐛𝐲 B𝐮𝐢𝐥𝐝𝐢𝐧𝐠 P𝐫𝐨𝐣𝐞𝐜𝐭𝐬. Finally, start doing projects. Begin with basic projects and gradually move to more complex ones. Apply the knowledge you’ve gained in SQL, Python, PySpark, and cloud services. As you gain confidence, tackle more complex projects that incorporate various data engineering techniques such as, #hadoop, #normalization, #denormalization, #datamodeling, .... and tools such as #git, #airflow, #docker, #dbt, #snowflake ... Document your projects thoroughly to showcase your skills, upload on #linkedln, #Github make visibility. Image Credit: Educative 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫, 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐫𝐚𝐭𝐡𝐞𝐫 𝐭𝐡𝐚𝐧 𝐣𝐮𝐬𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. 𝐍𝐨𝐭𝐞: Resource Lists with link given below in the comment. If you find it helpful, like the post & drop a comment saying 'helpful' Stay Active Nishant Kumar 🤝

You can't get a data analyst job without experience. You can't get experience without a data analyst job. This is the lie that keeps career changers stuck. Whenever I coach people into data roles, I show them a different path. You don't wait for experience. You create it. Here's exactly how: Step 1: Find a local nonprofit. Churches. Food banks. Youth programs. They all have messy data and no budget. Step 2: Offer free analytics work. Say this: "I'll analyze your data and give you a report with recommendations. No charge." They say yes. They always say yes. Step 3: Do real work. Take their Excel sheets. Clean the data. Build a dashboard. Create a slide deck with insights. Deliver it in 30 days or less. Step 4: Add it to your resume. Title it: "Analytics Consultant (Pro Bono Project)" Now you have real experience. Real results. A real story to tell in interviews. I've helped 1,000+ people get data jobs. The ones who land roles fastest? They stop waiting for permission. They create their own track record. No one needs to "give you a chance." You give yourself one. This isn't a hack. It's how professionals build credibility. And it works right now. What's stopping you from reaching out to a nonprofit this week?

How I’d Learn Data Engineering in 2025 (If I Were Starting Today) No fluff. No 10-hour YouTube rabbit holes. No course-hopping. Just one clear roadmap: the same one we follow at DataVidhya. 🧱 Phase 1: Get Strong at Python & SQL These are your bread and butter. Learn just enough Python to write clean scripts, work with APIs, and automate things. Master SQL joins, aggregations, and window functions. 🧩 Inside our program: You get hands-on coding with real interview-style SQL and Python problems. 🚀 Phase 2: Build an End-to-End Data Pipeline Not a toy project. Something you can actually explain in an interview. - Ingest raw data (APIs / CSVs / streaming) - Store it in cloud storage (S3) - Transform it (dbt / Spark) - Load into warehouse (Snowflake) - Visualize it (Looker Studio) 📦 What we give you: ✅ Real-world datasets ✅ Pre-configured cloud environments ✅ Projects like Spotify, Zomato, and Netflix ✅ Step-by-step videos + guided GitHub repo 🔁 Phase 3: Learn Airflow the Right Way Most beginners treat Airflow like a fancy cron job. But it’s the backbone of real pipelines in production. ⚡ Phase 4: Add Spark & Scale It This is where you go from “learning” to “engineering.” We teach Spark not just for syntax, but to handle distributed data, optimize joins, and manage memory. ⚡ In Code+, you can write and run PySpark code directly in the browser with test cases, no local setup, no EMR headaches. 💼 Phase 5: Get Interview-Ready It’s not about how many tools you know; it’s how well you can explain your decisions. We focus on questions like: - “Design a scalable pipeline for 1B records/day” - “How would you detect and handle data quality issues?” - “What happens when your job fails?” 🎯 TL;DR: What We’ve Built at DataVidhya: ✅ Learn the tools that matter (Python, SQL, dbt, Airflow, Spark) ✅ Build 16+ end-to-end projects ✅ Practice on a real coding platform — not just videos ✅ Prepare for interviews with our curated workbook #dataengineering #dataengineer

A job seeker came to me after 3.5 months of job searching with the following data: 180 applications submitted 12 screenings 1 referral 5 interviews 1 final round 0 offers After reviewing the data, I found that their job search was actually performing well in some areas but had key bottlenecks: - Strong application-to-screening rate Their resume and portfolio were doing well, getting them past the initial stage. - Good screening-to-interview rate Their performance in behavioral and situational questions was above average. - Weak interview-to-final round conversion  This indicated a struggle with: Technical rounds – Not demonstrating enough depth in core skills. Alignment with job descriptions – Answers weren’t tailored to the company’s needs. Surface-level responses – Not showcasing impact or real-world application of skills. The plan to improve: If I were coaching them, I’d focus on three key strategies: 𝟭) 𝗗𝗲𝗲𝗽 𝗜𝗻𝘁𝗼 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗦𝗸𝗶𝗹𝗹𝘀 Develop an interview strategy to explain technical and soft skills in-depth. Relate answers directly to the job description and company goals for higher impact. Use structured responses like the STAR method, but emphasize impact and problem-solving. 𝟮) 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 Daily practice of technical questions tailored to their target roles. Mock interviews to simulate real-world scenarios. Feedback loops to refine and improve responses. 𝟯) 𝗕𝗼𝗼𝘀𝘁 𝗥𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Increase outreach to professionals in their industry. Leverage networking and informational interviews to gain more referrals. Prioritize companies where referrals hold more weight. Key Points: ✔️ Data-driven job search analysis helps pinpoint areas that need improvement. ✔️ Fixing interview bottlenecks is often the key to securing more final rounds and offers. ✔️ Referrals still matter even in markets where they aren’t as strong as in the US or Canada. ✔️ Daily practice and structured preparation make a big difference in interview performance. By focusing on these areas, They could significantly increase their final round conversions and land a job faster. Have questions about your job search or how to break into data roles? Drop them in the comments, or send me a message. Let's get you to your next role! ------------------------ ➕Follow Jaret André  for more daily data job search tips.

Want to grow fast in data engineering? Start thinking in first principles. I get this question a lot: “What tools should I learn to get a data engineering job?” Here’s the truth: Tools are temporary. Principles are permanent. One company might be using Spark. Another might use an internal framework. Next year, they might switch to something entirely new. In this ever-evolving landscape, tools change. But what doesn’t change is the why and how behind them. Instead of chasing tools, ask deeper questions: • How is data distributed for processing? • What makes a good partitioning strategy? • How do you avoid data skew? • What affects node health and compute performance? • How can I reduce storage and compute costs? • How do I build for scale, fault tolerance, and reliability? These are first principles. Understand these well, and you can adapt to any tool—Spark, Flink, Snowflake, or whatever comes next. Tools are wrappers. Master the fundamentals, and tools will never limit you. #DataEngineering #FirstPrinciples #CareerAdvice #DistributedComputing #LearningMindset #BigData #TechGrowth

Trying to land your first data job but feel stuck in “learning mode”? You’re not alone. Most new analysts spend months on courses without knowing what hiring managers actually care about.  After years helping professionals break into data, here’s what I’ve learned:  Skills don’t speak for themselves, 𝘰𝘶��𝘱𝘶𝘵𝘴 do. If you’re just starting out, here’s the fastest way to build trust with recruiters (even without experience): 𝗦𝘁𝗼𝗽 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 “𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.” 𝗦𝘁𝗮𝗿𝘁 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘄𝗶𝘁𝗵 𝗶𝘁. That means: – Create one-page projects that answer real business questions  – Use tools you’re learning (SQL, Excel, Power BI, Python) to clean messy data  – Share insights in plain English don’t hide behind dashboards  – Post consistently and narrate your process like a consultant would You don’t need 10 certificates. You need 3 solid case studies that show how you think. 📌 If you’re targeting analyst roles, aim to solve:  ➝ How can we increase customer retention? ➝ Where are we losing money? ➝ What product is underperforming? These aren’t just data questions. They’re business problems solved with data thinking. You won’t master everything at once. But you can show you're learning like a pro. 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗳𝗶𝗲𝗹𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗮𝗰𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝘃𝗶𝘀𝗶𝗯𝗹𝗲. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁.