As a Data Engineer, you might spend most of your time with tools like Azure Data Factory, Databricks, or Spark. But behind every robust pipeline and transformation logic, Python often plays a silent but crucial role.

In my real-world projects — from cleaning raw datasets in Azure Databricks to automating blob cleanup in ADLS — these Python concepts have been non-negotiable.

Let’s dive into the 10 Python concepts every Data Engineer must master.

1. List Comprehensions

A concise way to create lists. Perfect for column filtering, schema manipulation, and small transformations.

columns = [col for col in df.columns if col != ‘unwanted_column’]

2. Lambda Functions

Lambda functions are anonymous, inline functions. They’re useful for quick transformations and often used in places like UDFs or map/filter operations.

double = lambda x: x * 2

3. Decorators

Helpful when logging ETL functions or adding retries to fragile jobs.Decorators are functions that modify the behavior of other functions or methods. They are great for tasks like logging, retries, and profiling, which are often essential in production code.

def logger(func):

def wrapper(*args, **kwargs):

print(f”Running: {func.__name__}”)

return func(*args, **kwargs)

return wrapper

4. Exception Handling

In data pipelines, things will go wrong. Handling exceptions gracefully allows for better monitoring and recovery.

try:

process_data()

except FileNotFoundError as e:

log_error(e)

5. Generators and Iterators

Generators are used to create iterators, which allow you to iterate through large datasets without loading everything into memory. This is crucial when working with big data or streaming data sources. Saves Memory.

def read_large_file(file):

for line in open(file):

yield line

6. Dictionaries (and defaultdict)

Perfect for lookup tables and fast aggregations. Dictionaries are excellent for storing key-value pairs, making data retrieval fast. The defaultdict from the collections module makes it easier to handle missing keys.

from collections import defaultdict

agg_data = defaultdict(int)

7. Context Managers

Context managers are helpful when dealing with resources like file handles, database connections, or temporary files. The with statement ensures that resources are properly cleaned up after use.

with open(“data.csv”) as file:

data = file.readlines()

8. Multi-threading & Multiprocessing

Useful for parallel API calls or partition-wise transformations (with caution). Parallelism can significantly speed up your data processing. Use multi-threading for I/O-bound tasks or multiprocessing for CPU-bound tasks. But, remember to be cautious when using them in production.

9. Working with JSON

A must for interacting with APIs, logs, and metadata in the cloud. Data from APIs, logs, and metadata files is often in JSON format. Knowing how to parse and manipulate JSON is a key skill for Data Engineers.

import json

parsed = json.loads(json_string)

10. Logging Module

Always better than print() in production. Logs to file, console, and supports levels like INFO, ERROR, etc. Replace print() with structured logging. The Python logging module allows you to log messages with different severity levels and is essential for tracking the health of your data pipelines.

import logging

logging.basicConfig(level=logging.INFO)

logging.info(“Job started”)

Final Thoughts

You don’t need to be a Python expert to succeed as a Data Engineer, but mastering these fundamentals will make your work smoother, cleaner, and more efficient.

Which concept do you use the most in your day-to-day projects?

About the Author

Azure Data Engineer|2x DP 203 , 1x Databricks Certified|Pyspark|Azure Databricks|MySQL|ADF|Apache Kafka|Delta Live Tables|Delta Lake|ADLS Gen 2|Python|Structured Streaming|Autoloader

Thorat, S (2025). 10 Python Concepts Every Data Engineer Must Know. Available at: 10 Python Concepts Every Data Engineer Must Know | LinkedIn [Accessed: 10th May 2025].