Navigating Data Careers

A lot of people trying to break into data science spend months, sometime even years... Learning the wrong things. They dive deep into neural networks, reinforcement learning, and complex machine learning algorithms, thinking that’s what will land them a job. But when they finally start applying, they realize the job market is looking for something else. So... what do companies want then? Most companies hiring data scientists aren’t looking for cutting-edge AI research. They need professionals who can: + Work with messy, real-world data – Cleaning, structuring, and analyzing data is 80% of the job. If you can’t handle raw datasets, machine learning skills won’t matter. + Use SQL fluently – If you can’t query a database efficiently, you’ll struggle in almost any data role. SQL is still one of the most in-demand skills in the field. + Apply basic statistical thinking – Companies don’t need fancy deep learning models for most problems. They need people who understand probability, regression, and how to make sense of data. + Communicate insights effectively – Data scientists who can translate numbers into clear, actionable recommendations will always be more valuable than those who just build models. + Understand the business problem first – Companies care about ROI, not algorithm complexity. If you don’t connect your work to business impact, you’ll be seen as just another technical hire. So... what mistakes are people doing? - Overloading on Theory Without Application – Learning every ML algorithm but never actually working on real datasets. - Ignoring SQL and Data Wrangling – Machine learning is useless if you can’t efficiently extract and clean data. - Building Portfolio Projects With No Business Impact – Instead of copying Kaggle projects, focus on solving problems that could help a company save money, improve efficiency, or make better decisions. How would I approach it? 1. Master SQL and data manipulation before diving into machine learning. 2. Prioritize problem-solving with real business datasets, not just pre-cleaned Kaggle data. 3. Learn to present insights clearly and tell a compelling data story. Focus on building projects that demonstrate impact, not just model accuracy. The data science job market isn’t looking for people who know the latest AI trends—it’s looking for people who can solve real problems with data. If you’re trying to break into the field, ask yourself: Are you learning what actually matters, or just what looks impressive on paper? Would love to hear your thoughts.

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

Job seekers are trapped in the "need experience to get a job, but need a job to get experience" cycle. Here is how you can break it: • Gain experience using public datasets: it's not about fancy machine learning projects. Start with cleaning, aggregating, and visualizing data in tools like Excel or Python in Google Colab. Find an interesting datasets from platforms like Kaggle, or US Goverment Open Data (https://data.gov/ ), or data from your city (e.g. Seattle's real-time fire 911 calls https://lnkd.in/gNEdS9Yk). ALWAYS create an artifact—a blog post, a GitHub repository, something to showcase. • Seek opportunities near you: Your uncle is running small business? They might need data insights. Your professor might be eyeing for someone to dissect student performance data. Reach out, offer your skills. Maybe you can collect your own data on your diet or sleep, and analyze it for yourself. (Data science YouTuber Ken Jee analyzed his own health data: https://lnkd.in/gf2SWNDq) No one is offering you a job? Create a job for yourself. • Leverage your current experience: maybe you are just learning data science but you have experience in other industries like marketing, finance, etc. You might not be the best data person, but you could be the person that knows more about the industry than an average data person, and knows more about about data than the average retailer. Leverage your current domain knowledge as a stepping stone, you don't have to start over completely. In the realm of data analytics, the world is your playground. Forget the traditional paths—carve out your own. There are multiple guests on my podcast started their career in non-tech roles. Experience isn't confined to job titles; it's crafted through initiative and passion. I interviewed a career coach who got into Google from non-tech background, learn more from our conversation: Apple: https://lnkd.in/gaM_cWP9 YouTube: https://lnkd.in/gCHTU94N Spotify: https://lnkd.in/g6fGuXzP #Datascience #Career

Want to break into a data analyst role? Use your current job as a training ground! Here is how you can prepare for your transition in your daily work: 1. 𝗨𝘀𝗲 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 Data is everywhere, no matter your current role. Start by using spreadsheets to track performance metrics or identify trends. Show that you can use data to support your decisions. 2. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗧𝗮𝘀𝗸𝘀 Use Excel formulas, Power Query, or basic Python scripts to automate repetitive tasks, freeing up your time and building valuable data manipulation skills. 3. 𝗩𝗼𝗹𝘂𝗻𝘁𝗲𝗲𝗿 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Look for opportunities within your company to work on data-related projects. It could be assisting a colleague with a report, or helping analyze customer data. These projects give you hands-on experience that you can add to your resume. 4. 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗖𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲𝘀 If your company has a data team, try to reach out to them. Ask if you can shadow or assist on small tasks. Learning directly from analysts will help you understand the real challenges they face and expand your network. Try to find an analyst who is willing to become your mentor. 5. 𝗕𝘂𝗶𝗹𝗱 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 If you create reports or present information in your current role, practice your data storytelling skills. Use Power BI, Tableau, or Excel to visualize data in a clear, and easily digestable way. 6. 𝗧𝗮𝗸𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 Many companies offer training and courses. Check if there are any analytics, Excel, or SQL courses available. Some companies will even reimburse external online lectures or full degrees. 7. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 Data analysts spend a lot of time understanding business needs. Practice working closely with different stakeholders in your current job. Try to understand their goals, challenges, and how you can help solve their problems using data. Start preparing for your transition to a data role right where you are! In our data-driven world, almost every position offers you the chance to practice the necessary data skills. Have you transitioned into data from another role, or are you planning to? I'd love to hear your experience! ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #jobtransition #careertransition #careergrowth  

Most people trying to break into data analytics in 2026 are following the same generic playbook as everyone else. And then wonder why they get the same lame results as everyone else. Here's the approach I'd take if I were starting from scratch today: 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘀𝗸𝗶𝗹𝗹𝘀: Excel, SQL, Power BI, and how to leverage AI effectively. These aren't optional. They're the foundation every hiring manager expects. The good news is there are structured courses that can get you job-ready faster than a degree. 𝗣𝗶𝗰𝗸 𝗮 𝗻𝗶𝗰𝗵𝗲: This is where most people go wrong. They try to be a "general" data analyst. Don't. Pick a domain: Marketing analytics, sales operations, healthcare reporting, finance. Specialization is what makes you memorable in a stack of 300 applicants. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘁𝗮𝗿𝗴𝗲𝘁𝗲𝗱 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼: 3 or more projects, all focused on your chosen niche. Not random Kaggle datasets. Projects that demonstrate you understand the business problems in that industry. This is what separates you from candidates who just list tools on a resume. 𝗡𝗮𝗿𝗿𝗼𝘄 𝘆𝗼𝘂𝗿 𝗿𝗲𝘀𝘂𝗺𝗲: Your resume should read like you were built for that specific domain. Leverage your background if you can. Tailor every bullet point. Remove the noise. Make it obvious you're not just another generalist. 𝗔𝗽𝗽𝗹𝘆 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆: 5 to 10 targeted applications daily. Not spray and pray. Targeted. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻: Continuously. Comment on posts. Connect with hiring managers. Initiate chats. Share your projects. This compounds over time in ways job boards never will. The tech job market is competitive. Always has been. That's not changing. But most candidates are competing on the same generic terms. You don't have to. Be specific. Be intentional. That's the difference maker.

I've reviewed 500+ Data Engineer resumes in the last 2 years. 80% get filtered in 6 seconds. Here's why — and what actually gets interviews 👇 🟦 𝟭. 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝘀𝗽𝗲𝗻𝗱 𝟲 𝘀𝗲𝗰𝗼𝗻𝗱𝘀 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗿𝗲𝘀𝘂𝗺𝗲 They scan in this order: → Job titles in your last 2 roles → Most recent company → Years of experience → ONE outcome line Everything else is decoration. 🟩 𝟮. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗮𝗿𝗲 𝘄𝗵𝗲𝗿𝗲 𝟴𝟬% 𝗳𝗮𝗶𝗹 Bad: "Built data pipeline using Airflow" Good: "Built CDC pipeline (Postgres → Kafka → Snowflake), 2M events/day, cut latency 6h → 8min" Numbers + outcome. Always. 🟧 𝟯. 𝗦𝗸𝗶𝗹𝗹𝘀 𝘀𝗲𝗰𝘁𝗶𝗼𝗻 = 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲, 𝗻𝗼𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗼𝗿 Listing 30 tools = junior signal Listing 5 with depth = senior signal 🟪 𝟰. 𝗢𝗻𝗲 𝗽𝗮𝗴𝗲. 𝗔𝗹𝘄𝗮𝘆𝘀. Two pages = "I don't know what's important." Even at Senior+. 🟥 𝟱. 𝗚𝗲𝗻𝗲𝗿𝗶𝗰 𝘀𝘂𝗺𝗺𝗮𝗿𝘆 = 𝗮𝘂𝘁𝗼-𝗳𝗶𝗹𝘁𝗲𝗿 Bad: "Data Engineer with 5 years of experience in big data..." Good: "DE who built data platforms for 2 fintech startups. Real-time pipelines (Kafka + Spark + Snowflake)." 🟨 𝟲. 𝗤𝘂𝗮𝗻𝘁𝗶𝗳𝘆 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗿 𝗶𝘁 𝗱𝗶𝗱𝗻'𝘁 𝗵𝗮𝗽𝗽𝗲𝗻 Bad: "Improved query performance" Good: "Reduced p95 latency 8s → 200ms, saved $40k/yr in compute" If you can't quantify it, recruiters assume it's fluff. 🟦 𝟳. 𝗙𝗼𝗿𝗺𝗮𝘁: 𝗯𝗼𝗿𝗶𝗻𝗴 𝘄𝗶𝗻𝘀 → Clean template (LaTeX / Notion / clean Word doc) → Black + white + ONE accent color → No icons, no skill bars, no photo → PDF only — never .docx Most DEs get rejected NOT because they lack skills. They lack a resume that signals their skills in 6 seconds. ----- Data engineers — what's the resume mistake you wish someone had told you earlier? 👇 ♻️ Repost if this saves someone from getting filtered. Follow 👉 Darshil Parmar for more practical Data Engineering

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.

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, 𝘰𝘶𝘵𝘱𝘶𝘵𝘴 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.

The Hard (and Surprisingly Popular) Way to Fail at Getting into Data Science: 1. Start by watching endless tutorials on every data-related topic, hoping the knowledge sticks through osmosis. 2. Panic after a couple of rejections and consider switching to a completely unrelated field—dog grooming, maybe? 3. Assume your resume will do the heavy lifting while completely ignoring the power of networking (spoiler: networking > resume). 4. Chase the next trendy tool like it’s a magic wand, without building a solid foundation in engineering or math. 5. Follow the crowd, focusing on what’s “hot” instead of what actually interests you, and end up with a cookie-cutter portfolio. 6. Apply to anything with “data” in the title, even if it’s an admin job or involves staring at spreadsheets all day. 7. Stuff your resume with buzzwords like “Spark” and “Big Data” even though the closest you’ve come to using them is reading a Medium article. 8. Set an unrealistic timeline: “If I’m not hired in six months, I’m throwing in the towel.” 9. Blame the universe for every rejection instead of adjusting your game plan. A Better, Smarter Approach to Breaking into Data Science: 1. Choose your adventure. Focus on areas that genuinely pique your interest—whether it’s NLP, computer vision, or something else that gets you excited. 2. Make networking your superpower. Building relationships with people in the industry can open doors you didn’t even know existed. 3. Learn from actual professionals. Forget just instructors—talk to people already doing the job to find out what skills they really use. 4. Work on projects that matter to you. When you’re passionate about a problem, your project will naturally stand out. 5. Find a mentor early. A good mentor can fast-track your learning and help you avoid costly mistakes. 6. Share your learning journey. Post regularly about what you’re working on, and you’ll build a community that supports you. 7. Consistency beats burnout. Slow and steady progress is better than trying to cram everything into a few intense weeks. 8. Get real-world experience early. Whether it’s freelancing, internships, or contributing to open-source projects, applying your skills is key. 9. Play the long game. Breaking into data science is a marathon, not a sprint. Persistence is what separates those who make it from those who quit too soon. Bottom Line: It’s about enjoying the process, learning along the way, and staying the course. There’s no magic formula—just perseverance and patience.