Putting your work on GitHub probably is the best decision you can make to build your portfolio. But what makes a good project stand out? A good README. Here are 4 ways to make a good README: 🧵

Analytics Engineer = translator between pipelines and BI. Stack: SQL • Git • dbt • Snowflake • Airflow. Deliverables: clean models, tests, docs, and a dashboard the business trusts. Start small, ship weekly, iterate.

nice add-ons: S3 for files, GitHub Actions for CI, MWAA to host Airflow, and dbt docs for the team. what would you swap in this stack?

guardrails: small warehouses per workload, 60s auto-suspend, query tags for cost. roles per layer + masking policies for PII. simple rules, big savings.

how it fits: Airflow pulls + lands data, then kicks off dbt. dbt builds RAW → INTERMEDIATE → REPORTING in Snowflake. clear ownership, quick debugging.

Snowflake = warehouse. elastic compute, separate storage/compute, auto-suspend/resume. zero-copy clone + time travel for safe testing and quick fixes.

dbt = transform. SQL-first models as code. tests out of the box. lineage + docs your execs can click. easy reviews in Git. keeps you portable across warehouses.

Airflow = orchestration. control run order, dependencies, retries, and alerts. schedule jobs, call APIs, watch files. one place to see the whole pipeline.

The “ultimate” data stack for small teams: Airflow + dbt + Snowflake. simple, fast, proven. here’s why:

Fivetran might by dbt labs. EL + T under one roof. Easier? maybe. Lock in? Also maybe. Curious how this plays out. https://t.co/agck4s5863

Dumping your data into S3 doesn’t make it a data lake. A real data lake has: - ✅ A catalog (what's in there) - ✅ Permissions (who can access what) - ✅ Structure (partitioning, schema) - ✅ Query tools (Athena, Redshift, etc) Without these you just have expensive storage.

Pretty dashboards are great — they should be visually appealing. But if they aren’t helping your team make better decisions, what’s the point? A good dashboard doesn’t just look good. It drives action. It answers real business questions. Are your dashboards helping your team

Your first job in analytics is closer than you think. Keep pushing! 💪

Upskill with Cloud & Advanced Tools: As you grow, learn tools that can give you an edge: •Cloud Platforms: AWS, GCP, or Azure. •Data Warehousing: Snowflake, Redshift, BigQuery. •Advanced SQL: Learn window functions, CTEs, and joins.

Network Like Crazy: Jobs are often found through connections. •Join data analytics communities (LinkedIn, Discord, Slack groups). •Attend meetups and conferences (virtual or local). •Reach out to professionals for advice – many are happy to help!

Showcase Your Work Online: Your portfolio isn’t enough if no one sees it. •Post on LinkedIn. •Write blog posts about your process on Medium. •Share visuals on platforms like Tableau Public.

Learn to Understand Business Problems: It’s not just about analyzing data – it’s about solving problems. Ask: •What does the business want to achieve? •How does this data help decision-making? Show that you can connect data to real-world outcomes.

Build a Portfolio: Your portfolio will make you stand out. •Use free datasets from Kaggle, Google Dataset Search, or open data portals. •Create: •Dashboards •SQL queries •Data cleaning projects

Learn ONE programming language: Start with Python (or R). •Python libraries like Pandas, Matplotlib, and Seaborn will help with data manipulation and visualization. •Don’t stress about coding perfectly. Focus on solving problems.

Start with the BASICS: Master the core tools you’ll use every day: •Excel/Google Sheets: For cleaning & analyzing data. •SQL: To extract and query data from databases. •Visualization Tools: Learn Tableau or Power BI to tell a story with your data.

Want to break into Data Analytics? Here’s your ultimate guide to learning the skills, building a portfolio, and landing your first job as a Data Analyst. 🧵👇