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.

It took me 6 years to land my first Data Science job. Here's how you can do it in (much) less time 👇 1️⃣ 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 — 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝘁𝗼 𝗶𝘁. I learned SQL and Python at the same time... ... thinking that it would make me a better Data Scientist. But I was wrong. Learning two languages at once was counterproductive. I ended up being at both languages & mastering none. 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙢𝙞𝙨𝙩𝙖𝙠𝙚: Master one language before moving onto the next. I recommend SQL, as it is most commonly required. ——— How do you know if you've mastered SQL? You can ✔ Do multi-level queries with CTE and window functions ✔ Use advanced JOINs, like cartesian joins or self-joins ✔ Read error messages and debug your queries ✔ Write complex but optimized queries ✔ Design and build ETL pipelines ——— 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝗶𝘁 As a Data Scientist, you 𝘯𝘦𝘦𝘥 to know Statistics. Don't skip the foundations! Start with the basics: ↳ Descriptive Statistics ↳ Probability + Bayes' Theorem ↳ Distributions (e.g. Binomial, Normal etc) Then move to Intermediate topics like ↳ Inferential Statistics ↳ Time series modeling ↳ Machine Learning models But you likely won't need advanced topics like 𝙭 Deep Learning 𝙭 Computer Vision 𝙭 Large Language Models 3️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 For me, this was the hardest skill to build. Because it was so different from coding skills. The most important skills for a Data Scientist are: ↳ Understand how data informs business decisions ↳ Communicate insights in a convincing way ↳ Learn to ask the right questions 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚: Studying for Product Manager interviews really helped. I love the book Cracking the Product Manager Interview. I read this book t𝘸𝘪𝘤𝘦 before landing my first job. 𝘗𝘚: 𝘞𝘩𝘢𝘵 𝘦𝘭𝘴𝘦 𝘥𝘪𝘥 𝘐 𝘮𝘪𝘴𝘴 𝘢𝘣𝘰𝘶𝘵 𝘣𝘳𝘦𝘢𝘬𝘪𝘯𝘨 𝘪𝘯𝘵𝘰 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦? Repost ♻️ if you found this useful.

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.

𝐘𝐨𝐮 𝐃𝐎𝐍'𝐓 𝐧𝐞𝐞𝐝 50 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠. You need 7 books. That's it. Most beginners jump straight into tools — Spark, Kafka, Airflow — without understanding how data systems actually work. Then they wonder why nothing connects. These 7 books fix that 👇 📘 Fundamentals of Data Engineering — Joe Reis & Matt Housley → Read this FIRST. Gives you the full picture before you touch any tool. 📘 Designing Data-Intensive Applications — Martin Kleppmann → The bible of distributed systems. Explains WHY systems fail at scale. 📘 Streaming Systems — Tyler Akidau → Makes Kafka, Flink, and Spark Streaming actually make sense. 📘 The Data Warehouse Toolkit — Ralph Kimball → Old but gold. Dimensional modeling that every DE should know. 📘 Data Engineering with Python — Paul Crickard → Theory to code. Build real pipelines with Python + Airflow. 📘 Data Pipelines Pocket Reference — James Densmore → Quick reference for pipeline patterns. Keep it on your desk. 📘 Designing Cloud Data Platforms — Zburivsky & Partner → Cloud architecture decisions explained clearly. Here's the order I'd recommend: 1 → Fundamentals of Data Engineering (understand the system) 2 → DDIA (understand how systems break) 3 → Data Warehouse Toolkit (understand modeling) 4 → Streaming Systems (understand real-time) 5 → Data Engineering with Python (start building) 6 → Data Pipelines Pocket Reference (quick patterns) 7 → Designing Cloud Data Platforms (cloud architecture) Reading builds intuition. Practice builds skills. You need both. I wrote a detailed breakdown of each book — what it teaches, what it won't help with, and when to read it. You can read it below ⬇️ Save this for later. Share it with someone starting out. ---- Follow Darshil Parmar for more data engineering content.

6 tips to land your first data analyst job (from someone who’s helped dozens do it) This is for the career changers. The new grads. The curious minds trying to break in. Here’s what actually works (not fluff): 1. Learn the right skills ↳ SQL, Excel, Python, Power BI 2. Get hands-on with real projects ↳ Kaggle. Personal dashboards. Side gigs. 3. Understand business context ↳ Know how data ties to decisions 4. Master your interview skills ↳ It’s not just about what you know, it’s how you explain it 5. Think smart, not hard ↳ Focus on leverage skills: storytelling, impact, automation 6. Create an impactful resume ↳ Your resume should say “I solve problems” I’ve seen these tips change careers. They can change yours too. Which one do you need to focus on most? ♻️ Repost this if someone in your network is job hunting!

As Data Analysts, we spend hours cleaning data, writing queries, building dashboards, and validating numbers. But no one prepares you for this moment: You present your insights… And someone says — “I don’t think this is right.” This is where most analysts struggle. Because handling pushback is a soft skill no one teaches — but every analyst needs. In the beginning of my career, I used to feel defensive. If someone questioned my numbers, I felt like they were questioning my ability. But over time, I realized something important. - Pushback is not rejection. - It’s part of decision-making. Here’s what I learned: First — don’t react, clarify. Ask calmly: - “Which part feels incorrect?” - “Is it the number or the interpretation?” Many times, the issue is not the data — it’s how it’s being understood. Second — separate ego from analysis. Your job is not to prove you’re right. Your job is to find the truth. If someone challenges your insight, go back to: – What’s the data source? – What’s the definition used? – What filters were applied? Be ready to explain your assumptions clearly. Third — understand stakeholder perspective. Sometimes the business leader has ground reality knowledge that data alone doesn’t show. For example: - Data shows sales dropped. - But sales head knows a major distributor went offline temporarily. That context matters. Fourth — document definitions and logic. When your numbers are transparent and well-documented, pushback reduces automatically. And finally — treat pushback as refinement. Many of my best insights improved because someone questioned them. Handling pushback well makes you look: - Confident - Mature - Business-ready Anyone can build a dashboard. Not everyone can defend insights calmly and logically. If you’re preparing for analytics roles, remember: - Technical skills get you the job. - Soft skills help you survive and grow.

A few years ago, breaking into data science meant learning Python, machine learning, and building a solid portfolio. That’s still important—but the job market is shifting, and many people are focusing on the wrong things. Companies are no longer just looking for "SQL experts" or "deep learning specialists." They want problem solvers who understand data, business, and execution. Companies are prioritizing practical, real-world data skills over advanced modeling. The ability to clean, analyze, and communicate insights is often more valuable than knowing how to fine-tune a neural network. AI is exciting, but many businesses still struggle with basic data infrastructure, and that's why companies need professionals who can: - Work with real, messy data instead of perfect Kaggle datasets. - Build dashboards and reports that drive actual decisions. - Explain findings to leadership in clear, non-technical language. Hybrid Roles Are on the Rise - The lines between data analyst, data scientist, and analytics engineer are blurring. Many companies expect data scientists to: + Know SQL and database management. + Understand cloud platforms and deployment. + Work closely with product teams, not just focus on models. What Should You Focus On to Stay Competitive? 1. Master SQL and Data Manipulation – Almost every data job requires it. 2. Strengthen Your Business Acumen – Companies care about insights, not just models. 3. Improve Your Communication Skills – If leadership doesn’t understand your findings, they won’t act on them. 4. Work on Real-World Projects – Hiring managers want to see impact, not just academic exercises. The best data professionals aren’t just great at coding—they understand how to use data to solve real business problems. If you’re learning data science today, ask yourself: Are you focusing on what hiring managers actually need, or just chasing what looks impressive on paper?

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 🤝

As an aspiring data analyst don't wait for your first role to gain experience! Here are 7 tips to land your first real-world role: 1. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Dive into datasets that interest you, like sports statistics or a topic relevant to your target industry. Personal projects showcase your passion and initiative. They also demonstrate your ability to analyze data, draw insights, and communicate findings without the guidance of courses. 2. 𝗙𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲 𝗼𝗿 𝗩𝗼𝗹𝘂𝗻𝘁𝗲𝗲𝗿: Offer your data analysis skills to local businesses, nonprofits, or startups. Real-world experience doesn’t have to come from a full-time job. Freelancing or volunteering helps you build a portfolio with visible results, while also expanding your network. 3. 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻𝘀: Participate in data challenges on platforms like Kaggle. Use them to test your skills and to give you projects to showcase in your portfolio. However, in the end, personal projects aimed at your interests or your target industry will be more impactful. 4. 𝗦𝗵𝗮𝗿𝗲 𝗬𝗼𝘂𝗿 𝗪𝗼𝗿𝗸 𝗣𝘂𝗯𝗹𝗶𝗰𝗹𝘆: Publish your analyses on LinkedIn, Medium, or a personal blog. Publicly sharing your work helps you build a personal brand, attract feedback, and demonstrate your expertise to potential employers. 5. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗪𝗲𝗯𝘀𝗶𝘁𝗲: Create a dedicated website or online portfolio to showcase your projects, skills, and achievements. Having a centralized place for your work makes it easy for potential employers to see your capabilities. Make sure to not only upload your code but also short descriptions of your projects. 6. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀: Attend industry meetups, webinars, and online forums to connect with experienced data professionals. Building relationships within the industry can open doors to mentorship, collaboration, and job opportunities, helping you gain insights and guidance on your journey. 7. 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 𝗼𝗻 𝗚𝗿𝗼𝘂𝗽 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Join or form study groups or online communities where you can work on data projects collaboratively. Group projects help you gain experience in teamwork, communication, and problem-solving—skills that are in high demand in real-world data roles. A great example of such a community is Break Into Data. You don’t need to wait for your first job to start building experience. Create your own opportunities, and let your work speak for itself. How are you building your experience as an aspiring data analyst? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #workexperience #portfolio #careergrowth