Navigating Data Careers

Once you’ve worked in Data Engineering (8 years like me) long enough, you realize tools don’t matter as much. ➥ Whether it’s Airflow or Dagster At its core, it’s just orchestrating dependencies and running jobs on a schedule. The syntax changes, the UI gets fancier, but the underlying challenge is the same: can you build reliable pipelines that never miss a beat, even when something fails at 2 AM? ➥ Whether it’s Spark or Dask At its core, it’s about distributed computation and memory-efficient processing. Sure, Spark’s APIs might feel different from Dask’s, but you’re always wrestling with partitioning, shuffles, and squeezing every ounce of performance out of your cluster before the bill shows up. ➥ Whether it’s Kafka or Pulsar At its core, it’s event streaming, buffering, and pub-sub. The configuration files change, but the real work is designing robust consumer groups, managing offsets, and making sure no critical event gets dropped or duplicated, especially when things scale. ➥ Whether it’s Snowflake, BigQuery, or Redshift At its core, it’s columnar storage, distributed querying, and cost-optimized warehousing. UI, pricing models, or integrations might look shiny, but the tough part is always designing schemas for future analytics, tracking costs, and tuning performance for the business. ➥ Whether it’s dbt or custom SQL pipelines At its core, it’s transformation, testing, and version control of business logic. dbt gives you modularity and lineage, but your biggest wins come from nailing reusable models, data tests that actually catch issues, and making sure every logic change is trackable. ➥ Whether it’s Parquet, Delta, or Iceberg At its core, it’s about data formats optimized for query performance and consistency. New formats will keep appearing, but the big lesson is understanding partitioning, versioning, schema evolution, and choosing what actually fits your use case. Tools come and go. The icons on your resume might change every few years. But fundamentals like: ➥ Data modeling (can you design for flexibility and performance?) ➥ Scalability (will it survive 10x more data or users?) ➥ Latency (does your pipeline deliver data when the business needs it?) ➥ Lineage (can you explain how that metric was built, step-by-step, a year later?) ➥ Monitoring & recovery (will you be the one getting that 3AM pager?) Those are the real make-or-break skills. Focus on what stays true, not just what’s new.

If you’re AI-curious but can’t decide where to start, this one’s for you 👇 The AI space is vast. Buzzwords fly. Roles overlap. And it’s easy to get stuck wondering: 👉 Should I become a Data Scientist, ML Engineer, or Product Manager? Instead of chasing titles, map your strengths and figure out where you fit best in the AI lifecycle. 📌 I put together this infographic + a blog post to help you find your lane, with 10 clear roles you can actually train for (even without a PhD or a Stanford badge). 🚀 The 10 Career Paths in AI, Simplified: ➡️ AI/ML Researcher or Scientist – creating new algorithms, publishing papers, pushing the frontier ➡️ Applied ML Scientist / Data Scientist – solving real-world problems with models and experimentation ➡️ ML Engineer / MLOps / Software Engineer (ML) – taking models to production and scaling them ➡️ Data Engineer – building the infrastructure to move and manage data ➡️ Software Engineer – writing core product code with ML components ➡️ Data Analyst – analyzing data to drive insights and business impact ➡️ BI Analyst – working with KPIs, reporting, and decision frameworks ➡️ AI Consultant – advising teams and clients on adopting AI responsibly ➡️ AI Product or Program Manager – aligning AI capabilities with user needs and business goals ➡️ Hybrid Roles – wearing multiple hats across technical and strategic functions 🧭 How to choose the right one for you: → Start with your natural strengths: coding, communication, business thinking, or data sense → Identify the part of the AI lifecycle you enjoy most: research - build - deploy - iterate → Stack the right skills intentionally: • Coders: Python, PyTorch, prompt design, eval frameworks • Data Infra: SQL, Spark, Airflow, Lakehouse, vector DBs • Insights: Analytics, causal reasoning, dashboard tools • Translators: AI roadmap building, governance, storytelling → Focus on shipping evidence of work: demo apps, notebooks, open-source PRs, or experiments → Develop a T-shaped skill profile – go deep in one role, but stay conversational across others 💡 A few truths to keep in mind: → You don’t need to be a “10x coder” to work in AI → Problem-solving > job titles → Projects > perfect resumes → Cross-functional skills are a force multiplier – clear writing, ethical reasoning, and stakeholder empathy go a long way → There’s no “entry-level” in AI – just entry-level impact 📖 Curious to explore deeper? Check out the full blog, and save the infographic to use as a compass for your AI journey: https://lnkd.in/daQNHPyg

Data Scientists, Engineers, Analysts—these roles are exploding, with data science jobs projected to 𝐠𝐫𝐨𝐰 𝟑𝟔% 𝐛𝐲 𝟐𝟎𝟑𝟏, according to BLS—one of the fastest-growing professions. Meanwhile, according to Gartner 𝟔𝟏% 𝐨𝐟 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 are evolving their data strategies to keep up with AI-driven disruption. But let’s be honest: job titles don’t tell the full story. Here’s what these roles actually do: • 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 – 𝐓𝐡𝐞 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐫𝐬 They design the structure that makes everything else possible—data lakes, warehouses, and pipelines that ensure information moves efficiently and securely. Without them, data would be a tangled mess. • 𝐃𝐚𝐭𝐚 𝐀𝐥𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐬 – 𝐓𝐡𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐫𝐞𝐚𝐭𝐨𝐫𝐬  They don’t just analyze data; they extract value from it. Using machine learning, statistical modeling, and predictive analytics, they turn raw data into business-changing insights. • 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐯𝐞𝐬 – 𝐓𝐡𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝐅𝐢𝐧𝐝𝐞𝐫𝐬 They specialize in uncovering trends, correlations, and anomalies. Whether it’s identifying fraud, optimizing operations, or finding revenue opportunities, their job is to make sense of the noise. • 𝐃𝐚𝐭𝐚 𝐖𝐡𝐢𝐬𝐩𝐞𝐫𝐞𝐫𝐬 – 𝐓𝐡𝐞 𝐀𝐈 𝐇𝐚𝐧𝐝𝐥𝐞𝐫𝐬  They prepare data for AI, ensuring it’s clean, structured, and optimized for machine learning models. Because feeding bad data into AI is like training a GPS with a 10-year-old map. • 𝐃𝐚𝐭𝐚 𝐎𝐫𝐚𝐜𝐥𝐞𝐬 – 𝐓𝐡𝐞 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬  They predict what’s coming next—market trends, customer behavior, risk factors. Using historical data and predictive models, they help businesses make proactive decisions. • 𝐃𝐚𝐭𝐚 𝐒𝐮𝐫𝐠𝐞𝐨𝐧𝐬 – 𝐓𝐡𝐞 𝐂𝐥𝐞𝐚𝐧-𝐔𝐩 𝐂𝐫𝐞𝐰  They fix bad data, remove errors, and ensure consistency. Because even the best algorithms are useless if they’re working with garbage. • 𝐃𝐚𝐭𝐚 𝐏𝐡𝐢𝐥𝐨𝐬𝐨𝐩𝐡𝐞𝐫𝐬 – 𝐓𝐡𝐞 𝐄𝐭𝐡𝐢𝐜𝐬 & 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐆𝐮𝐢𝐝𝐞𝐬  They ask the big questions: Should we use this data? Is it biased? Does it comply with privacy laws? They ensure data-driven decisions are also responsible ones. With Chief Data Officers now overseeing AI strategy at 58% of organizations, the importance of these roles is only growing. So, which one best describes what you do? Or do you have a better title for your role? Drop it in the comments! 𝐅𝐨𝐫 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞: https://lnkd.in/eM6c3FkG ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

I just talked to an aspiring data analyst who took a popular bootcamp and still feels inadequate for their job search. Why? The bootcamp taught WIDE skills instead of DEEP. I stood there stunned hearing about how they learned a little bit of Tableau and a little bit of Power BI and a little bit of SQL, etc. but didn't learn anything super well on a deeper level. These basic high-level skills are fluffy, and anyone can learn them. They aren't helping you stand out on the job market and probably barely teach you enough to build a project. Why learn 2 BI tools (Tableau AND Power BI) when you could learn 1 deeply and transfer the skills to others in the future? Instead of learning a little bit of everything, focus on max 2-3 tools and learn them deeply. It'll make all the difference in your job search and career. Don't underestimate transferrable skills.

You applied to 100+ jobs but no interviews? Here's what's actually happening. Your experience is valuable. You're just invisible. Let me explain why, and how to fix it. When you apply online, your resume goes into a database called an ATS (Applicant Tracking System). Think of it like a massive filing cabinet. Now here's the key: Some recruiters don't read every resume. They search. Just like you search Google, they search their database: "Python AND data analysis" "SAFe AND agile transformation" "Tableau AND dashboard" If your resume doesn't have their exact search terms, you’re making it harder to get discovered. You're not rejected. You're just not found. But here's the secret: The job description often tells you EXACTLY what keywords they'll search for. It's like having the answer key. Example from a real job posting: If they say "Experience with Snowflake required"... → They'll search "Snowflake" → Make sure you write "Built data warehouse in Snowflake…" Not "cloud database" or "modern data platform." Use their exact words: Snowflake. I've mapped out 80 keywords that get candidates noticed in 2025: Top searches happening right now: • Python, TensorFlow, LangChain (AI roles) • Kubernetes, Terraform, Docker (tech leadership) • Power BI, Tableau, SQL (data leadership) • SAFe, Agile, DevOps (transformation roles) Your action plan: 1. Read the job description carefully 2. Circle every tool, platform, or methodology mentioned 3. Add those EXACT terms to your resume (if you have that experience) 4. Use them naturally in your accomplishments Example: Instead of: "Led team through digital modernization" You say: "Led SAFe agile transformation using ServiceNow and Jira, reducing delivery time by 40%" You have the experience. Now make it searchable. Your next role isn't rejecting you. It just hasn't found you yet. You’ve got this! 💡 Save this cheat sheet of 80 searchable keywords ♻️ Share to help someone in your network Follow me for more insider recruiting insights

You don’t need to “learn data.” You need to pick your lane in the data world. And most people have no idea what the lanes even are. Let me break it down ↓ 🧱 Data Engineer They’re the builders. Set up the pipelines. Move the data. Make sure it's clean, fast, and available. Think: Python, Airflow, BigQuery, AWS 📊 Data Analyst They’re the interpreters. Use the data. Visualize the trends. Drive decisions with charts, not opinions. Think: SQL, Tableau, Excel 🤖 Data Scientist They’re the predictors. Build models. Forecast future outcomes. Answer “What if?” and “What’s next?” Think: Python, Scikit-learn, ML, storytelling When I help career-changers find their fit in tech, I tell them: 🛣️ Engineers build the road 🚗 Analysts drive the car 🧠 Scientists design the autopilot It’s not about “which role is better.” It’s about which one makes you light up. So, which lane are you choosing? ⬇️ Comment “Engineer,” “Analyst,” or “Scientist.”

Back in 2020, being a Data Analyst often meant being a generalist — handling everything from reporting to modeling, sometimes even engineering tasks. But fast forward to 2025, and the landscape looks very different. Now, we’re seeing a growing demand for specialized roles like: ✔️ Product Analyst ✔️ Marketing Analyst ✔️ Risk Analyst ✔️ Power BI Developer ✔️ Healthcare Analyst …and many more. This shift reflects the increasing complexity of data challenges and the need for deeper domain expertise. As someone navigating the data field, I find it both exciting and essential to keep sharpening skills in specific areas while staying curious about the bigger picture. 💡 Tip: Whether you’re just starting out or already in the field — focus on a niche, but learn to collaborate across roles. That’s where real impact happens. 👉 Which of these roles are you working toward or exploring? I’d love to hear your path. #DataAnalytics #CareerGrowth #DataAnalyst2025 #PowerBI #SQL #ProductAnalytics #Specialization #LinkedInLearning

As a recruiter for top tech companies, I’ve reviewed 1,000+ resumes. You only need to get these 5 sections right to land 6-figure interviews. 1. Positioning Statement Forget the generic “motivated team player” summary. Your top section should tell me in 3 lines: - Who you are - What kind of problems you solve - Where you’ve done it Example: “Backend engineer with 4 years of experience scaling infra at early-stage startups. Shipped distributed systems handling 50M+ requests/day. Currently focused on latency, observability, and developer experience.” If this section is clear, I’ll keep reading.  If it’s vague, I won’t. 2. Experience (But Structured Like a Case Study) Instead of dumping tasks, each role should answer: - What were you hired to do? - What did you actually build or own? - What changed because of your work? Bullet points should reflect results, not responsibilities. Redesigned caching logic → reduced API latency by 47% across 3 services. Led incident response for system outage → cut recovery time by 60%. That’s what hiring managers remember. 3. Company/Team Context Especially if you worked at a large company, give 1 line of context. “Worked on the Ads ML Infrastructure team at Meta, supporting $XXB in annual revenue.” It helps recruiters understand the scale and environment — fast. 4. Projects Section (Optional, but powerful) For newer engineers or people transitioning into tech, 1-2 serious projects can carry a resume. But only if you show real thinking and impact. Instead of: Built a web app using React and Node. Try: Built a budgeting tool used by 800+ users; integrated Stripe and Plaid APIs, reduced error rate to <0.3%. Show that you didn’t just code, you shipped. 5. Skills That Support the Story Don’t list everything you’ve ever touched. List the tools, stacks, and domains that match what you’re applying for. And reinforce them in your bullet points. “Python” in your skills section means nothing if your experience doesn’t prove you’ve used it in real scenarios. Your resume's job isn’t to tell your life story. It’s to get you in the room. If yours isn’t built to convert, it’s time to rethink it. Repost if this helped. P.S. Follow me if you are a job seeker in the U.S.  I talk about resumes, job search, interview preparation, and more. 

Here's a secret about the data industry that I don't think too many people realize... nearly everyone is struggling with the "best practices." When I started my data career, I thought the companies I worked for were struggling. However, when I started creating content and interviewing other leaders, I realized it was not just me. Once I joined a data infrastructure vendor, it became clear that it was an industry-wide challenge. I've been on 100+ discovery calls and countless deep-dive calls with companies ranging from startups to Fortune 500-- every single one has made it clear to me how hard the basics are to do in data. IT IS NOT A SKILL PROBLEM! Data is just that hard to work with and is in a constant state of chaos. Here are a few examples of why: • The startup that initially focused on "best practices" ultimately scaled quickly and had to refactor its tech stack... which increased complexity. • The mid-sized company pivoted in its business model, and now the data needs to support two different workflows simultaneously as it transitions customers to a new product. • The massive enterprise company acquired seven companies in the past year, and merging these systems has been painful, yet the data is business critical. Even if you reach the promised land of "best practices" within your company, it will likely be short-lived as the company inevitably changes. What have you seen in your data career?

This is how I would get a job in the next 50 days if I graduated today: Exactly a year ago, I graduated without a job offer in hand. Within the next 50 days, I already had my first offer. Here are the 5 steps I would follow to get a job in the current market: 1) Mass Apply: A highly saturated market coupled with a slowing economy leaves us with only one option to convert a job offer, Mass Applications. Although this approach has been a point of debate for a long time, I have seen this trend especially work for individuals who are newcomers in the market and have less amount of relevant experience as compared to others. 2) Target key skills: In most of my interviews, these 3 skills were common - SQL, Excel, and Tableau. I would begin by becoming proficient in them. You can start by learning their basics from YouTube (Tons of great free material out there) and Udemy. Next, I would build portfolio projects. To be exact – 2 for SQL, 1 for Excel, and 1 for Tableau. 3) Informational interviews: There is nothing better than learning from someone who has already walked the path that you are stepping onto. If you get an interview invite from a company, one of the first things I would recommend would be to align a call with someone who’s already working in a similar role to guide you through the process. Even if you are not looking to learn about a specific company, learning from their best practices on how they tackled their interviews could work wonders in your own process. 4) Learn what you don't know: We all have read those funny memes where a job description asks for 30+ years of experience for an entry-level 20-year-old applicant. Already this might not be true in reality, many jobs nowadays ask for a ton of knowledge that all of us might already possess. Hence instead of not knowing about an important topic related to your role, it’s better to have some basic knowledge of the same before your interview. A quick LinkedIn learning or Udemy course that you could do within a few days can be a great resource to prepare for those tough technical interviews. 5) Cracking the interview: For the big day, practice common interview questions and be prepared to discuss your projects in detail. Your story is your unique selling point so make sure to develop a crisp and impactful pitch that you will deliver to increase your chances of receiving an offer. Bonus Tip (Turn the tables): Once your interview is done, be prepared with detailed questions that you may have for the interviewer. This shows your interest and level of research to the interviewer and can highly increase your chances of leaving a golden impression at the end. I know many of you are graduating this week or have just graduated, if you think it’s hard to get a job in this challenging market, I hope this post can provide guidance. Finally, I have faith in you and with patience & persistence, I'm sure you'll land the right opportunity soon. All the best for your journey, and keep growing!