The Only SQL Skills You Need to Be a Data Engineer

SQL is the most important skill for a Data Engineer.

Python is great, but SQL is the language of data. If you cannot write good SQL, you cannot be a Data Engineer.

But you do not need to know everything. You do not need to be a Database Administrator. You just need to know how to transform and move data.

Here is the exact list of SQL topics you need to master to get hired.

Part 1: The Foundation (Retrieving Data)

Goal: Getting the data you want.

These are the commands you will use every single day. You must be able to write these without looking them up.

• Basic Queries: SELECT, FROM, WHERE, ORDER BY, LIMIT.

• Filtering: Using AND, OR, IN, BETWEEN.

• Aggregations: Summarizing data using COUNT, SUM, AVG, MIN, MAX.

• Grouping: Using GROUP BY and HAVING to categorize your results.

Part 2: Cleaning Data (Transformation)

Goal: Fixing bad data.

Data is never clean. It always has spelling mistakes, missing numbers, or weird formats. As a Data Engineer, your job is to fix this.

• Handling Nulls: Using COALESCE to replace empty values with a default (like 0).

• String Fixing: Using TRIM (remove spaces), UPPER/LOWER (fix capitalization), and SUBSTRING.

• Logic: Using CASE WHEN statements. This is your "If/Else" logic in SQL.

  • Example: "IF price is null THEN set price to 0."

• Date Functions: Using DATE_TRUNC or formatting dates.

Part 3: Combining Data (The "Merge")

Goal: Bringing data together.

You will rarely look at just one table. You need to join data from different sources (like "Customers" and "Orders").

• Joins: You must understand the difference between INNER JOIN, LEFT JOIN, and FULL JOIN.

  • Tip: 90% of the time, you will use Left Joins.

• Unions: Combining two lists into one long list (UNION and UNION ALL).

Part 4: The "Engineer" Level (Advanced Logic)

Goal: Solving complex problems.

This section separates the beginners from the pros. These tools make your code cleaner and faster.

• Window Functions:

  • ROW_NUMBER() and RANK(): Useful for removing duplicates (deduplication).

  • LEAD() and LAG(): Useful for comparing today's data with yesterday's data.

• CTEs (Common Table Expressions):

  • Stop writing messy "Subqueries" inside other queries. Use WITH statements to make your code easy to read.

Part 5: Building Structures (DDL & DML)

Goal: Creating the system.

This is the big difference between a Data Analyst and a Data Engineer. Analysts only read data. Engineers create tables and insert data.

• Creating Tables: CREATE TABLE and DROP TABLE.

• Changing Data: INSERT, UPDATE, and DELETE.

• Upserts (MERGE): How to update a row if it exists, or insert a new one if it doesn't.

The Skills Checklist

Can you do everything on this list?

1. The Basics

• [ ] I can Filter data using WHERE, IN, and LIKE.

• [ ] I can Aggregate data using GROUP BY.

2. Transformation

• [ ] I can fix NULL values using COALESCE.

• [ ] I can write CASE WHEN logic.

• [ ] I can format Dates and Strings.

3. Advanced Logic

• [ ] I understand LEFT JOIN vs INNER JOIN.

• [ ] I can use ROW_NUMBER to find the "latest" record.

• [ ] I use CTEs (WITH clause) to keep my code clean.

4. Engineering

• [ ] I can CREATE a new table.

• [ ] I can INSERT data into a table.

• [ ] I know when to use an Index to make a query faster.

Summary

Focus on writing Clean Code (CTEs) and Efficient Logic (Window Functions).

If you can take messy raw data, clean it with SQL, and insert it into a new table, you have the skills of a Data Engineer.