Junior Data Engineer Skills Checklist

1. Core SQL Skills

Most junior data engineer roles are SQL-heavy.

Must Know

• SELECT, WHERE, ORDER BY

• JOIN (INNER, LEFT, RIGHT)

• GROUP BY, HAVING

• Aggregate functions (COUNT, SUM, AVG)

• Subqueries

• Common Table Expressions (CTEs)

• CASE WHEN

• Handling NULL values

Good to Have

• Window functions (ROW_NUMBER, RANK, LAG)

• Basic query optimization understanding

• Writing readable, clean SQL

✅ If you’re weak in SQL, no other skill will compensate.

2. Data Modeling Fundamentals

Junior roles won’t expect you to design complex systems, but you must understand structure.

Must Know

• What is a fact table vs dimension table

• Basic star schema

• Primary keys & foreign keys

• Normalized vs denormalized data

Good to Have

• Slowly Changing Dimensions (SCD – Type 1 & 2)

• Naming conventions

3. ETL / ELT Basics

Almost every JD mentions data pipelines.

Must Know

• What is ETL vs ELT

• Extracting data from:

  • Databases

  • CSV / JSON files

  • APIs (basic understanding)

• Transforming data using SQL

• Loading data into a warehouse

Tools Often Mentioned

• Airflow (basic DAG understanding)

• Informatica / Talend / Glue (any one)

• dbt (in modern stacks)

👉 Concept > Tool at junior level.

4. Programming Language (Python Preferred)

You don’t need to be a software engineer.

Must Know

• Python basics

• Reading & writing files

• Working with lists, dicts

• Simple functions

• Basic error handling

Good to Have

• Pandas (read CSV, basic transformations)

• Writing small scripts for automation

🚫 Advanced OOP is not required for junior roles.

5. Databases & Data Warehouses

Must Know

• Difference between:

  • OLTP vs OLAP

• At least one relational database:

  • PostgreSQL / MySQL / SQL Server

Good to Have

• Cloud data warehouses:

  • Snowflake

  • BigQuery

  • Redshift

You should know why warehouses are used, not internal architecture.

6. Cloud Fundamentals (High Demand)

Almost every JD mentions cloud.

Must Know

• What is cloud computing

• Basic services:

  • Storage (S3 / GCS)

  • Compute (EC2 / VM)

• IAM basics (roles, permissions – high level)

Good to Have

• One cloud platform:

  • AWS / GCP / Azure

• Running simple jobs on cloud

7. Data Quality & Validation

This is often hidden in JDs but very important.

Must Know

• Handling missing data

• Duplicate records

• Basic validation checks

• Understanding bad vs good data

Good to Have

• Logging

• Simple monitoring ideas

8. Version Control (Often Ignored, Still Expected)

Must Know

• Git basics

• Clone, commit, push

• Working with branches (basic)

You won’t be tested deeply, but not knowing Git is a red flag.

9. Linux & Command Line Basics

Must Know

• Navigating directories

• Basic commands (ls, cd, grep, cat)

• Running scripts

10. Soft Skills (Yes, They Matter)

Junior data engineers are expected to learn fast.

Recruiters Look For

• Ability to explain your SQL logic

• Asking the right questions

• Documentation mindset

• Willingness to debug data issues

Final Reality Check

You do NOT need:

• Kafka mastery

• Spark internals

• Distributed system design

• Complex algorithms

You DO need:

• Strong SQL

• Clear data thinking

• Pipeline fundamentals

• Curiosity and consistency

If you are preparing for your first data engineering role, focus on depth over tools.
Master SQL, understand data flow, and build small projects - tools can be learned on the job.