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stage 1 — programming foundations (logic clarity)

goal: think clearly in steps.

• if-else mastery (edge cases)
• loops (for, while, nested loops)
• functions (pure functions vs stateful)
• basic math problems (factorial, prime, fibonacci, digit sum)
• string manipulation (reverse, palindrome, frequency count)

focus: clarity > cleverness.

stage 2 — dsa (only what data engineers actually use)

goal: structured thinking.

• arrays / lists (traversal, slicing)
• strings (parsing heavy)
• hashmaps / dictionaries (very important)
• sets (uniqueness logic)
• stack (balanced brackets)
• queue (basic simulation)
• binary search (understand idea, not obsession)

core idea: lookup efficiency and grouping.

stage 3 — small system projects (local, python only)

goal: state + structure.

• crud cli app
• todo app with file persistence
• inventory tracker (prevent negative stock)
• grading system (average + ranking)
• log analyzer (parse file → summary stats)
• lru cache using class

focus: state consistency + error handling.

stage 4 — sql mastery checklist

goal: set-based thinking.

phase 1 — fundamentals

• select all columns
• select specific columns
• filter with where
• sort with order by
• limit rows
• count rows
• max / min / avg
• basic conditions (between, like)

phase 2 — grouping

• group by city
• sum revenue per user
• max order per user
• count per day
• having clause
• filter grouped data

phase 3 — joins

• inner join users and orders
• left join with null handling
• users without orders
• product revenue aggregation
• top users by spending
• most sold product
• products never ordered

goal: relational clarity.

phase 4 — intermediate logic

• subqueries (above average spending)
• second highest value
• window function ranking
• running totals
• first order per user
• duplicate detection
• percentage contribution

window functions = intellectual maturity in sql.

phase 5 — business analysis

• cohort analysis
• retention rate
• churn detection
• category revenue per month
• anomaly detection vs user average

this is analytics engineering thinking.

stage 5 — data engineering core projects

goal: pipeline thinking.

very easy — pipeline awareness

• csv cleaner with logging
• daily aggregation script
• json to csv transformer
• log parser
• data quality checker

focus: validation mindset.

easy — structured pipelines

• simple etl (csv → transform → sqlite)
• scheduled ingestion (process new files only)
• api ingestion with retry
• bronze/silver/gold folder structure
• streaming simulator (rolling summary)

focus: repeatability.

intermediate — reliability thinking

• incremental load with watermark
• change data capture simulation
• parquet-based mini lakehouse
• anomaly detection pipeline
• airflow dag with dependencies

focus: state + orchestration.

moderate hard — production mindset

• event-driven pipeline (producer → queue → consumer)
• schema evolution handling
• idempotent batch job
• analytics api serving layer
• full mini platform (ingest → transform → store → serve → log)

focus: robustness.

stage 6 — engineering maturity

• add logging everywhere
• add error handling
• simulate failure cases
• run job twice (ensure no duplicates)
• document architecture diagram
• measure execution time
• add simple monitoring metrics