Drew Zeimetz

"I’m making $82K as a Data Analyst. And I’m terrified someone will ask me to explain my dashboard." That message landed in my inbox last week. From someone in Texas. Computer Science degree. Bootcamp certificate. Great job title. Zero confidence. And this isn’t rare anymore. I keep seeing the same pattern across the US: ✅ Completed online courses ✅ Built portfolio projects ✅ Passed technical interviews ✅ Landed analyst or BI roles But once inside the job… Reality hits. Because corporate data work is very different from tutorials. You’re suddenly expected to: → Explain why metrics changed → Fix slow reports used by leadership → Understand messy production datasets → Communicate insights clearly → Defend design decisions And nobody trained you for that. So what happens? You Google during meetings. You copy code patterns without context. You avoid speaking up. You second-guess every decision. Eventually imposter syndrome hits hard. Here’s the truth most people won’t say: The problem isn’t intelligence. It’s preparation gap. Courses teach tools. Jobs require thinking. I worked with someone recently — let’s call him Mike. Marketing Analyst. Strong Excel background. Transitioned into a Power BI role. Salary jumped fast. But he struggled with: • Data modeling logic • Measure design strategy • Query optimization • Stakeholder communication We focused on practical skill-building: Week 1–2 Audit of his reports and identifying structural issues Week 3–4 Rebuilding models with performance focus Week 5–6 Scenario-based questioning and explanation drills Week 7–8 Live problem-solving using his real datasets Outcome: Confidence improved Meeting participation increased Manager feedback turned positive Because career growth isn’t about collecting certificates. It’s about mastering decision-making under real constraints. If you’re working in analytics right now and feel stuck between beginner and expert — that’s normal. But staying there isn’t required. This is exactly why I run my 1-to-1 mentorship program — not generic courses, not recorded videos — real guidance focused on YOUR work and YOUR gaps: 📊 Fixing dashboards you’re building today 💾 Strengthening SQL reasoning 🎯 Practicing real workplace scenarios 📈 Improving communication with stakeholders 🧠 Building true professional confidence If you’re ready to stop guessing and start growing — let’s talk. Comment 💡 or send me “MENTOR” and I’ll share full details. #DataAnalytics #PowerBI #BusinessIntelligence #SQL #AnalyticsCareer #CareerGrowth

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🎯 Day 1/10: 100 SQL Scenario Questions After conducting 200+ interviews, I know EXACTLY what questions trip up candidates. Starting a 10-day series to help you crack SQL interviews with confidence! These aren't textbook questions - they're real scenarios I've asked in interviews and seen in hiring processes. 📌 Save this series 👉 Swipe through for Day 1 questions 🔔 Follow for Day 2 tomorrow Whether you're switching to IT or leveling up your career, this series is your interview prep roadmap. #SQL #DataAnalytics #PowerBI #InterviewPrep #CareerChange #DataWithSawan

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Fully aligned with this vision, regardless of the data-visualization tool being used, true value can only be achieved when insights are designed with—and for—end users. This means focusing on directly actionable KPIs that exist not just to look good, but to drive real decisions and spark meaningful actions.

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As a Full-Stack Data Analyst, I'd say 80% Data Engineer is quite accurate. Being well-equipped to code Python, SQL, and Javascript is a big plus with today's standard of "Data Analyst", though delineation of responsibilities could not hurt, especially on corporate-level data management and engineering.

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Stop treating your brain like a Database. Treat it like a Processor. I used to store content ideas in my head. A conversation with a friend would spark an idea, and I’d think, "I'll remember that for next week's post." But as Data Analysts, we know that Memory (RAM) is volatile. If you don't write the data to the disk (Storage), it gets wiped out when you shut down (or go to sleep). I finally learned that ideas are everywhere: - in comments - emails - daily chats - group community literally everywhere. But they are useless if they stay in your RAM. You need to "Brain Dump." You need to perform an ETL (Extract, Transform, Load) on your own thoughts: - Extract: Get the idea out of your head immediately. - Transform: Turn it into a rough draft or bullet point. - Load: Save it to your Notion or content calendar. Since I started physically "dumping" my ideas out of my head, my content anxiety has disappeared. Don't trust your RAM. Write it down. What tool do you use for your "Second Brain"? #ProductivityHacks #ContentStrategy #ETL #PersonalGrowth

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Don’t apply for 50 Data Science jobs. You only need ten. If you’re applying to 50 companies, they’re across wildly different industries. Which means you probably don’t care about most of them. And if you don’t care about the product or mission, it shows. - Generic CVs. - Weak answers in interviews. - Low energy when things get hard. Instead, focus on a small number of companies you’re genuinely excited about. Where you care about the problem they’re solving. That’s exactly what my recent client James did. He applied to just ten roles. And got an offer from a leading dating app. Not because he was luckier. But because his applications and interviews were far stronger. So don’t “pray and spray” applications. Be selective. If you want more help running a calmer, more effective job search, I share free insights in my weekly newsletter: https://lnkd.in/eTAmRsDu

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𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗛𝗮𝗿𝗱𝗲𝗿 𝗧𝗵𝗮𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 Hot take. Most people get excited about regression models and predictive analytics. But in healthcare? Data wrangling determines whether your model even makes sense. You can build the most beautiful model… But if: • ICD codes were grouped incorrectly • Null values weren’t handled • Cohorts weren’t defined properly Your prediction is noise. In my experience, 60–70% of the work is data cleaning and validation. Modeling is the final layer — not the foundation. Agree or disagree? 〰️Kosi Clean data. Clear decisions. Better healthcare.

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🚩 Dashboards aren’t supposed to look like Christmas trees 🎄 Have you ever open a dashboard and just sit there like, “Wait… what am I even looking at?” Charts everywhere. Colors shouting at you. 3 minutes in, and still no clue what’s important. That’s not insight. That’s noise. 📍 Dashboards aren’t built to impress. They’re built to guide decisions. → More visuals ≠ more value → One clear story > ten half-stories → If people feel lost, the dashboard failed 📍 Your dashboard isn’t for you. It’s for the person who needs to act on it. → If it looks like a Christmas tree, you built it for yourself. →If it looks like a compass, you built it for the business. 📌Drop one dashboard tip or advice so others can learn #dataliving #favouribude

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Summer of SQL Week 5 - Foodie Fi Working on your SQL skills without data to take reps with is really hard. Most practice sets often provide too clean of data, far too unrealistic for job applications. Summer of SQL does not have this problem. These problems give just enough guidance to help you troubleshoot the errors, while maintaining the effort on your end to apply your knowledge. This week provided two tables, plans and subscriptions. Subscriptions change after a first week trial period, whenever a customer elects to change their plan type, or if the customer "churns" or exits the service. All of these are tracked in the subscriptions data, so comparing user changes requires different SQL skills than your base tutorials. This attached image shows a single query aimed to solve a problem - break down the count and percentage breakdowns for all 5 plan options as of 12/31/2020. Since the plans change in the same columns, namely the start date and plan types, it requires some self-joining logic that allows direct comparison of user activity. Wrapped in the "anti" CTE is a comparison of the subscriptions table for all like customer_id values. Knowing that any plan changes occur sequentially and adding parameters to the query makes it so that all updates are tracked in this self-join. The anti-join step is made by excluding rows with a matching counterpart, or said another way, that there is no latest update to the customer plan before the 12/31/2020 date. By wrapping this in a CTE, I was able to perform simple aggregates to determine the count and percentage of all plan types by the desired end date. SQL like this feels like a business-applied logic puzzle. This puzzle requires knowing the different steps needed to get to the prompted result. When no prompt is given, as is often true as an analyst, you are looking to find the most telling performance metrics from the data. Also - you can tell I actually wrote this, not AI. My inconsistencies in capitalization for "as" was done with intent to remind you that not every AI needs to know company table info. Look for more resources on Python and SQL for data applications at my GitHub: https://lnkd.in/gRB__mEk

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Great reminder that dashboards aren’t built in tools. They’re built in conversations. The real work is clarifying decisions, defining signals, and aligning stakeholders before a single visual is drawn. Tools only amplify the thinking.

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Day 43 of my data skills update learning from Zach Wilson and and DataExpert.io was the one where I turned my capstone project in! After 6 weeks of learning the analytics engineering tools and the data engineering process and the hard work that I put into coding and building I was able to turn in my project. Time to wait on grading, but I'm proud of myself for this. Here are a few things I learned: - The Google Analytics API does not have a user_id or session_id to allow for easy joining. - The Google Analytics API also doesn't give you latitude and longitude for mapping. So I had to put the map I wanted to make on the backburner for the DE capstone. - When setting up a connection to Snowflake in Airflow, you will get an "access" error if you also have the Snowflake connection url as part of your task in your Airflow DAG. - The free version of Tableau doesn't have a Snowflake connection. After the grading has been done and I pass, I will share my dashboard for everyone to see.

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Big companies have data teams. You have... yourself and maybe a bookkeeper. Good news: You don't need a data warehouse. You need 3 simple layers. 𝗟𝗔𝗬𝗘𝗥 𝟭: 𝗥𝗔𝗪 𝗗𝗔𝗧𝗔 Where transactions happen. - Bank feeds - POS system - Invoicing software - Payment processors (Stripe, PayPal) This is messy and that's OK. It's the raw material. 𝗟𝗔𝗬𝗘𝗥 𝟮: 𝗖𝗟𝗘𝗔𝗡 𝗗𝗔𝗧𝗔 Where transactions get organized. - Your accounting software (QuickBooks, Xero) - Transactions categorized - Duplicates removed - Reconciled to bank This is your single source of truth for money. 𝗟𝗔𝗬𝗘𝗥 𝟯: 𝗜𝗡𝗦𝗜𝗚𝗛𝗧𝗦 Where you actually make decisions. - Monthly P&L - Cash flow forecast - Dashboard with KPIs - Trends over time Most small businesses stop at Layer 1. Some get to Layer 2 (eventually). Few ever reach Layer 3 consistently. 𝗧𝗵𝗲 𝗴𝗮𝗽 𝗶𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻. Raw → Clean should happen automatically (bank feeds, rules, integrations) Clean → Insights should be one click (reports, dashboards) When it's manual, it doesn't happen. When it's automatic, you actually use it. Which layer is broken in your business? #DataArchitecture #SmallBusiness #BusinessIntelligence #Automation

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