Data Analyst Career Growth

I’ve never used Tableau. I’ve never used Power BI. I’ve never used Looker. Yet apparently these are hot hot right now. However, your TOOLS are not what make you a fantastic analyst. What makes you a fantastic analyst is how you: - Build relationships - Think about problems - Understand the business - Communicate your insights The frustrating part is that most of us receive plenty of formal training on the tools and methods. But we’re left on our own to figure out the things that actually make a difference. So how do you build these skills? —— 1. Watch the best analysts around you. Pay attention to how they operate. What questions do they ask in meetings? How do they organize their findings? How do they explain complexity without sounding confusing or condescending? Steal their techniques. Test them out. Make them your own. —— 2. Read, read, read. Not just analytics books. Read business books, behavioral science, writing, storytelling, psychology, marketing. The broader your lens, the sharper your thinking becomes. And sharper thinking = better insights + clearer communication. (Bonus: It gives you metaphors and mental models that make your insights stick.) —— 3. Be interested in people.     Influence starts with trust. Build relationships outside of your team. Ask what people are working on. Share useful context when you can. Offer to help someone debug a spreadsheet. Pass along an article they might like. —— You've got the technical chops. Now it's about influence, clarity, and connection. ♻️ Repost to help other analysts stop stressin about needing to learn ALL THE TOOLS P.S. Want to build these skills in 5 minutes a week? Join 1,300+ analysts getting tips to their inbox every Tuesday. Just tap “View my newsletter” at the top of this post. 👋🏼 I’m Morgan. I write about data viz, storytelling, and how to make your insights actually land with your audience.

Data Nerds! I ranked every data engineering tool by how often it shows up in 4M+ job postings. 📊 But here's the catch 😳. Some critical skills show up way less than they should because they're often assumed as foundational skills for jobs. (e.g., Skills like Bash/Terminal for running pipelines) Anyway, here's the breakdown of the tiers 👇 (Note: % = how often each tool appears in DE job postings) 🔴 S TIER — Non-Negotiable The core skills needed for any DE job. Don't apply without these: 📊 SQL (~68%) — every warehouse runs on it. Query, transform, and model data. 🐍 Python (~67%) — the pipeline language. Ingestion, automation, APIs, glue between systems. ⌨️ Terminal/Bash (~11%) — every tool you'll use runs from here. This is highly undervalued in postings. 📁 Git (~11%) — version control. Every team uses it. Same posting-% caveat as Bash. ☁️ One cloud platform + warehouse (~26-46%) — AWS + Redshift, GCP + BigQuery, or Azure + Synapse. Combined cloud presence is in nearly every posting. Start with SQL, then Python. Everything else you absorb alongside them. 🟠 A TIER — Job-Ready Foundation The tool that closes the gap from "learning DE" to "hireable for modern stacks": 🪛 dbt (~10%) — only 10% of all DE postings, but 36% in Analytics Engineer (AE) roles. That's not a niche, it's a leading indicator. AE is the new hybrid role modern data teams are hiring for: part analyst, part engineer. ✅ Land the job with S + A. Pass the interview with conceptual knowledge of B Tier 👇 🟡 B TIER — Interview-Aware Know what they solve. Don't expect to code from scratch: ⚙️ Airflow (~17%) — orchestration. Built on DAGs (directed acyclic graphs). ⚡ Spark (~38%) — distributed computing for processing large datasets. 🌊 Kafka (~19%) — real-time event streaming between systems. All these depend on a foundational knowledge of Python & SQL; don't jump the gun learning these. 🟢 C TIER — Data Platform Awareness Pick the one your company uses. Understand both conceptually: ❄️ Snowflake (~26%) — pure SQL warehouse. Optimized for analytics. Modern-stack favorite. 🧱 Databricks (~24%) — lakehouse on Spark. Handles structured + unstructured. ML/AI heavy teams. 🔵 D TIER — Versatility Multipliers Lower headline demand, but high value per hour: 📊 Power BI (~15%) / Tableau (~10%) — but the kicker: in AE roles these jump to 28% / 33%. Modern data teams want pipeline builders who can also visualize. For analysts pivoting to DE, lead with this in interviews. 🟣 E TIER — Path-Dependent High demand on paper, but concentrated in legacy enterprise stacks. Skip until your job requires it: ☕ Java (~25%) — legacy enterprise data infrastructure ⚖️ Scala (~22%) — Spark's native language. Spark-heavy shops. 🎥 How did I derive this ranking? In my latest video, I walk through the concepts first (the DE lifecycle, what each tool actually solves) and then derive the tiers. (Link in comments 👇)

𝐖𝐡𝐞𝐧 𝐈 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐦𝐲 𝐟𝐢𝐫𝐬𝐭 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐣𝐨𝐛, I thought the most important thing was just writing clean SQL or building dashboards. But over time, I’ve realized — that’s just 30% of the job. There are so many small but super important things I wish someone had told me early on: ✅ 𝐍𝐞𝐯𝐞𝐫 𝐚𝐬𝐬𝐮𝐦𝐞 𝐰𝐡𝐚𝐭 𝐬𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫 𝐰𝐚𝐧𝐭𝐬 Always reconfirm. For Example - “Do you want revenue by order date or delivery date?” This one clarification can save hours of rework. ✅ 𝐒𝐭𝐚𝐫𝐭 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐥𝐨𝐠𝐢𝐜 Add comments in your queries. Note assumptions. Future you (and your team) will thank you later. ✅ 𝐁𝐮𝐢𝐥𝐝 𝐟𝐨𝐫 “𝐰𝐡𝐚𝐭 𝐢𝐟” 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 Don’t just show total sales. Add flexibility: what if someone asks, “Show it by product?” or “Can I filter by channel?” Design with curiosity in mind. ✅ 𝐃𝐨𝐮𝐛𝐥𝐞-𝐜𝐡𝐞𝐜𝐤 𝐝𝐚𝐭𝐚 𝐟𝐫𝐞𝐬𝐡𝐧𝐞𝐬𝐬 This one’s underrated. Many analysts get stuck explaining why the dashboard still shows last month’s data. Always know your refresh cycle. ✅ 𝐊𝐞𝐞𝐩 𝐛𝐚𝐜𝐤𝐮𝐩𝐬 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐪𝐮𝐞𝐫𝐢𝐞𝐬 𝐚𝐧𝐝 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 Especially in big companies — sudden access loss, role changes, or tool migrations can make you lose months of work. Keep local copies. ✅ 𝐉𝐨𝐢𝐧 𝐫𝐞𝐯𝐢𝐞𝐰 𝐜𝐚𝐥𝐥𝐬 & 𝐬𝐡𝐚𝐝𝐨𝐰 𝐝𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧𝐬 Even if you’re not presenting — just listening to how senior folks talk about metrics, ask follow-up questions, or challenge assumptions helps you think better. Honestly — it’s not just SQL or Python that makes you a better analyst. It’s these small habits that no one teaches, but they compound over time. If you’re just starting your journey — save this post and revisit whenever you feel stuck.

10 things every data analyst should know, but rarely, someone teaches you.  1. 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝗼𝗳𝘁𝗲𝗻 𝗱𝗼𝗻'𝘁 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝘄𝗮𝗻𝘁. You have to help them define it.       2. "𝗝𝘂𝘀𝘁 𝗼𝗻𝗲 𝗺𝗼𝗿𝗲 𝗺𝗲𝘁𝗿𝗶𝗰" 𝗶𝘀 𝗻𝗲𝘃𝗲𝗿 𝗷𝘂𝘀𝘁 𝗼𝗻𝗲 𝗺𝗼𝗿𝗲. Learn to push back politely.       3. 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗻𝗲𝘃𝗲𝗿 𝗰𝗹𝗲𝗮𝗻. Get really good at validating and cleaning data.       4. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗮𝘁𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆. A simple bar chart with a clear story wins.       5. 𝗦𝗽𝗲𝗲𝗱 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. A quick answer today is often better than a perfect answer next week.       6. 𝗬𝗼𝘂’𝗿𝗲 𝗻𝗼𝘁 𝗮 𝗿𝗲𝗽𝗼𝗿𝘁 𝗯𝘂𝗶𝗹𝗱𝗲𝗿. You’re a problem solver with data.       7. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗴𝘂𝗶𝗱𝗲 𝘆𝗼𝘂. Learn what drives revenue and cost.       8. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝗹𝗶𝗸𝗲 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗲𝗹𝘀𝗲 𝘄𝗶𝗹𝗹 𝗿𝗲𝗮𝗱 𝗶𝘁. Because they will.       9. 𝗬𝗼𝘂𝗿 𝗿𝗲𝗮𝗹 𝗷𝗼𝗯 𝗶𝘀 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲, 𝗻𝗼𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀. Make sure your insights get acted on.      10. 𝗞𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴. Tools change, but curiosity and clarity will always win. Which hit hardest for you, or what would you add to the list? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post helpful. 💾 𝗦𝗮𝘃𝗲 this for your future self. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #stakeholdermanagement #softskills #careergrowth

How can Data Engineers leverage the open-source AI stack to build innovative solutions? Storage and Vector Operations: ->PostgreSQL with pgvector enables storing and querying embeddings directly in your database, perfect for semantic search applications. ->Combine this with FAISS for high-performance similarity search when dealing with millions of vectors. ->For example, you can build a document retrieval system that finds relevant technical documentation based on semantic similarity. Data Pipeline Orchestration: ->Netflix's Metaflow shines for ML workflows, allowing you to build reproducible, versioned data pipelines. ->You can create pipelines that preprocess data, generate embeddings, and update your vector store automatically. ->Useful for maintaining up-to-date knowledge bases that feed into RAG applications. Embedding Generation at Scale: ->Tools like Nomic and JinaAI help generate embeddings efficiently. ->You can build batch processing systems that convert large document repositories into vector representations, essential for building enterprise search systems or content recommendation engines. Model Deployment Infrastructure: ->FastAPI combined with Langchain provides a robust framework for deploying AI endpoints. ->You can build APIs that handle both traditional data operations and AI inference, making it easier to integrate AI capabilities into existing data platforms. Retrieval and Augmentation: ->Weaviate and Milvus excel at vector storage and retrieval at scale. ->Can be used to build systems that combine structured data from your data warehouse with unstructured data through vector similarity, enabling hybrid search solutions that leverage both traditional SQL and vector similarity. Here are some Real-world applications that can be explored: ➡️ Document intelligence systems that automatically categorize and route internal documents Ref: - Building Document Understanding Systems with LangChain: https://lnkd.in/gFgfSbwr - Learn Vector Embeddings with Weaviate's Documentation: https://lnkd.in/g96ym4BJ - pgvector Tutorial for Document Search: https://lnkd.in/gue4gzcs ➡️ Customer support systems that leverage historical ticket data for automated response generation Ref: - RAG (Retrieval Augmented Generation) with LlamaIndex: https://lnkd.in/gAM6_2fv ➡️ Product recommendation engines that combine traditional collaborative filtering with semantic similarity Ref: - FAISS for Similarity Search: https://lnkd.in/gTuCgyBE - AWS Personalize: https://lnkd.in/ggNar5xU ➡️ Data quality monitoring systems that use embeddings to detect anomalies in data patterns Ref: - Great Expectations: https://lnkd.in/g7JjGjBu - Azure ML Data Drift: https://lnkd.in/geYTXBXd Inspired by: ByteByteGo #dataengineering #artificialintelligence #innovation #ML #cloud

20 signs you're working with an effective data analyst: Everyone thinks it's about advanced algorithms and complex dashboards. But real data excellence comes from methodical habits that build trust and deliver insights. Here are 20 signs of a truly effective analyst 👇 1. They document every step of their analysis ↳ Clear notes make their work reproducible and trustworthy 2. They check data quality before the analysis begins ↳ They know garbage in = garbage out; always validate first 3. They use version control religiously ↳ Every code change is tracked, and nothing gets lost 4. They explore data thoroughly before diving in ↳ Understanding context prevents critical misinterpretations 5. They create automated scripts for repetitive tasks ↳ Efficiency isn't just nice—it's necessary for scale 6. They maintain a reusable code library ↳ Smart analysts never solve the same problem twice 7. They test assumptions with multiple validation methods ↳ One test isn't enough; they triangulate confidence 8. They organize project files logically ↳ Their work is navigable by anyone, not just themselves 9. They seek peer reviews on critical work ↳ They know fresh eyes catch blind spots 10. They continuously absorb industry knowledge ↳ Learning never stops; trends change too quickly 11. They prioritize business-impacting projects ↳ Every analysis connects directly to decisions 12. They explain complex findings simply ↳ Technical brilliance means nothing without clarity 13. They write readable, well-commented code ↳ Their work lives beyond them, accessible to others 14. They maintain robust backup systems ↳ Data loss isn't an option they're willing to risk 15. They learn from analytical mistakes ↳ Errors become stepping stones, not stumbling blocks 16. They build strong stakeholder relationships ↳ They know data needs people to make it valuable 17. They break complex projects into manageable chunks ↳ Progress comes through disciplined, incremental work 18. They handle sensitive data with proper security ↳ Compliance isn't optional—it's foundational 19. They create visualizations that tell clear stories ↳ They know a picture needs a narrative to drive action 20. They actively seek evidence against their conclusions ↳ Confirmation bias is their constant enemy The most valuable analysts aren't the ones with the most tools. They're the ones with the most rigorous practices. Which of these habits could transform your data work today?

Junior Data Engineers jump straight into the code. Senior Data Engineers solve these 10 problems first: 1️⃣ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 → Do they actually need a pipeline? → What problem is your pipeline solving? → What is the expected business outcome? 2️⃣ 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗔𝗹𝗹 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀 → Where is every input coming from? → Do you have access & permissions? → How much data needs to be extracted? 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗙𝗿𝗲𝘀𝗵𝗻𝗲𝘀𝘀 & 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 → Real-time or batch? → Is daily/weekly/monthly enough? → When does the business need the data available? 4️⃣ 𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 𝗩𝗼𝗹𝘂𝗺𝗲 & 𝗚𝗿𝗼𝘄𝘁𝗵 → What are the retention requirements? → How much data will you process per day? → How much storage will you need in 1 year? 5️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁 → What happens if upstream sends bad data? → What SLAs/SLOs exist for availability and delivery? → What types and formats should producers guarantee? 6️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹 & 𝗚𝗿𝗮𝗶𝗻 → Star schema or wide table? → Do they need the lowest-level granularity? → Will this model scale as new use cases appear? 7️⃣ 𝗣𝗹𝗮𝗻 𝗳𝗼𝗿 𝗦𝗰𝗵𝗲𝗺𝗮 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 → Is your downstream model flexible? → How will you handle new fields being added? → What happens if the source schema changes? 8️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗥𝘂𝗹𝗲𝘀 → Handle nulls → Handle duplicates → Define business validation rules 9️⃣ 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Where should you add logs? → How will alerts trigger and who receives them? → What should you monitor: latency, volume, freshness? 🔟 𝗣𝗹𝗮𝗻 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 & 𝗕𝗮𝗰𝗸𝗳𝗶𝗹𝗹𝘀 → Where will you store backups? → How will you reprocess historical data if needed? → How do you avoid double-counting during backfills? Following these steps guarantees a 𝗿𝗼𝗯𝘂𝘀𝘁, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, and 𝗳𝗿𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻-𝗳𝗿𝗲𝗲 data pipeline. Data Engineers, what did I miss?📝 --- ♻️ Repost if you agree planning > coding 🔔 Follow José for more daily Data Engineering tips

You will fall behind as a Data Engineer if you don't adapt to these things ⬇️ Remember when data engineering was simple? Extract, transform, load. Those days might start to feel like ancient history now. The data engineering role is shifting; it's time to adapt to new things. 🤖 The AI Integration Reality AI has become part of everything we do at this point. Earlier, we used to process data only for businesses, but now we also need to process data for AI models. This means getting comfortable with: - Document preprocessing - Embedding generation - Vector database management - Building retrieval systems that power conversational AI Your carefully crafted datasets aren't just feeding reports anymore. They're the foundation for intelligent applications that users interact with directly. 🚄Real-Time Has Become Real The streaming revolution isn't coming; it's here What used to be a "nice-to-have" for select use cases has become table stakes for competitive data operations. Kafka isn't exotic infrastructure anymore, it's standard plumbing. Spark Structured Streaming has evolved from experimental to essential. 👨🏻💻 Platform Thinking Replaces Pipeline Thinking The most significant shift in data engineering is the move from building individual solutions to creating reusable platforms. Modern data teams are implementing: - CICD for data workflows: everything is getting automated here - Product-oriented approach: dedicated person who owns the data and manages it - Developer experience optimization for downstream consumers - Integrated testing throughout the development lifecycle You're not just moving data anymore, you're building the foundation that enables entire organizations to make data-driven decisions. The most successful data engineers I know aren't the ones who can write the fastest Spark job or memorize every Kafka configuration. They're the ones who can look at a system and immediately spot the future failure points, bottlenecks, and technical debt that will cripple the team in a year. #dataengineer #dataengineering

The one skill that separates senior data analysts from juniors is not SQL, Python, or any other technical tool. It’s business acumen. A lot of people think that moving from junior to senior is about mastering SQL, Python, or advanced statistics. But the biggest differentiator isn’t a technical skill. It’s understanding the business. At the junior level, your job is to pull data, clean it, and build reports. At the senior level, you’re expected to understand the why behind the data instead of just delivering numbers. You need to ask the right questions rather than just answering data requests. You should be able to prioritize what matters because not all data is useful. The best analysts focus on the metrics that drive revenue, efficiency, or cost savings. You must be able to communicate insights rather than just sharing data. A table full of numbers isn’t enough. You need to translate data into a story that executives can act on. To build business acumen, start by learning the metrics that drive your company. Understand revenue, churn, customer acquisition cost, and other key business metrics. When analyzing data, always ask yourself how it impacts the business. Think like an owner. If this were your company, what decisions would you make based on your analysis? Technical skills get you hired. Business acumen makes you invaluable. The analysts who grow into senior roles are the ones who move beyond pulling data to driving strategy. What do you think? Is business acumen the key to leveling up in analytics?