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Data Engineer with Python Salary in 2024

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Total:
140
Median Salary Expectations:
$5,227
Proposals:
1

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Data Engineer developer with Python with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.

The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.

Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.

Data Engineer

What is a data engineer?

A data engineer is a person who manages data before it can be used for analysis or operational purposes. Common roles include designing and developing systems for collecting, storing and analysing data.

Data engineers tend to focus on building data pipelines to aggregate data from systems of record. They are software engineers who put together data and combine, consolid aspire to data accessibility and optimisation of their organisation’s big data landscape.

The extent of data an engineer has to deal with depends also on the organisation he or she works for, especially its size. Larger companies usually have a much more sophisticated analytics architecture which also means that the amount of data an engineer has to maintain will be proportionally increased. There are sectors that are more data-intensive; healthcare, retail and financial services, for example.

Data engineers carry out their efforts in collaboration with particular data science teams to make data more transparent so that businesses can make better decisions about their operations. They use their skills to make the connections between all the individual records until the database life cycle is complete.

The data engineer role

Cleaning up and organising data sets is the task for so‑called data engineers, who perform one of three overarching roles:

Generalists. Data engineers with a generalist focus work on smaller teams and can do end-to-end collection, ingestion and transformation of data, while likely having more skills than the majority of data engineers (but less knowledge of systems architecture). A data scientist moving into a data engineering role would be a natural fit for the generalist focus.

For example, a generalist data engineer could work on a project to create a dashboard for a small regional food delivery business that shows the number of deliveries made per day over the past month as well as predictions for the next month’s delivery volume.

Pipeline-focused data engineer. This type of data engineer tends to work on a data analytics team with more complex data science projects moving across distributed systems. Such a role is more likely to exist in midsize to large companies.

A specialised, regionally based food deliveries company could embark upon a pipeline-oriented project, building an analyst tool that allows data scientists to comb through metadata to retrieve information about deliveries. She could look at distances travelled and time spent driving to make deliveries in the past month, and then input those results into a predictive algorithm that forecasts what those results mean about how they should do business in the future.

Database centric engineers. The data engineer who comes on-board a larger company is responsible for implementations, maintenance and populating analytics databases. This role only comes into existence where data is spread across many databases. So, these engineers work with pipelines, they might tune databases for particular analysis, and they come up with table schema using extract, transform and load (ETL) to copy data from several sourced into a single destination system.

In the case of a database-centric project at a large, national food delivery service, this would include designing an analytics database. Beyond the creation of the database, the developer would also write code to get that data from where it’s collected (in the main application database) into the analytics database.

Data engineer responsibilities

Data engineers are frequently found inside an existing analytics team working alongside data scientists. Data engineers provide data in usable formats to the scientists that run queries over the data sets or algorithms for predictive analytics, machine learning and data mining type of operations. Data engineers also provide aggregated data to business executives, analysts and other business end‑users for analysis and implementation of such results to further improve business activities.

Data engineers tend to work with both structured data and unstructured data. Structured data is information categorised into an organised storage repository, such as a structured database. Unstructured data, such as text, images, audio and video files, doesn’t really fit into traditional data models. Data engineers must understand the classes of data architecture and applications to work with both types of data. Besides the ability to manipulate basic data types, the data engineer’s toolkit should also include a range of big data technologies: the data analysis pipeline, the cluster, the open source data ingestion and processing frameworks, and so on.

While exact duties vary by organisation, here are some common associated job descriptions for data engineers:

  • Build, test and maintain database pipeline architectures.
  • Create methods for data validation.
  • Acquire data.
  • Clean data.
  • Develop data set processes.
  • Improve data reliability and quality.
  • Develop algorithms to make data usable.
  • Prepare data for prescriptive and predictive modeling.

Where is Python used?

Web Crawling Shenanigans

    • Python slinks through websites like a ninja, snatching data and whispering '404 error' as a joke when pages evade capture.

AI's Kitchen

    • Python stirs the AI pot, tossing in a pinch of algorithms and a dollop of data to cook up some truly mind-nibbling intelligence.

Game of Codes



    • In the realm of game development, Python plays the jester, not the king, but it still juggles codes and enchants indie developers.



Astronomy's Telescope Lens Polisher



    • Python keeps its head among the stars, polishing data from the cosmos and helping boffins unlock the universe's cheat codes.


Python Alternatives

 

Java

 

Object-oriented programming language used for enterprise applications, mobile apps, and large systems development.

 

Example: Android app development

 


// Python code
def greet(name):
return "Hello, " + name + "!"

# Java equivalent
public class HelloWorld {
public static String greet(String name) {
return "Hello, " + name + "!";
}
}




    • Runs on billions of devices worldwide.
    • Static typing can lead to fewer runtime errors.
    • Comes with a rich set of APIs and a vibrant ecosystem.
    • Verbose syntax compared to Python.
    • Slower development time due to explicit compilation.
    • Can be more challenging for beginners.




JavaScript

 

The scripting language primarily for the web, used in front-end development and increasingly in back-end with Node.js.

 

Example: Interactive websites, server applications

 


// Python code
def add(x, y):
return x + y

# JavaScript equivalent
function add(x, y) {
return x + y;
}




    • Essential for client-side web development.
    • Highly versatile with frameworks like React, Angular, and Vue.
    • Event-driven non-blocking I/O with Node.js.
    • Dynamic typing can lead to runtime errors.
    • Asynchronous programming can be complex.
    • Fragmented ecosystem due to rapid evolution.




Go (Golang)

 

A statically-typed language designed at Google, known for its simplicity and high performance in concurrent operations.

 

Example: Cloud services, distributed networks

 


// Python code
def add(x, y):
return x + y

# Go equivalent
func add(x int, y int) int {
return x + y
}




    • Optimized for multi-core processors with built-in concurrency.
    • Statically-typed with a clean and readable syntax.
    • Efficient execution and a strong standard library.
    • Limited third-party libraries compared to Python.
    • Interface-based type system can be tricky.
    • Less versatile for certain applications.

 

Quick Facts about Python

 

Monty Python's Love Child

 

Let's kick things off with a chuckle: Python, a coding language that's as much about fun as function, was born in the late '80s thanks to a chap named Guido van Rossum. He was on a quest to combat the drudgery of the season (think Christmas with no presents) and ended up crafting this nifty script-slinger in 1989. But here's the twist—it's named after the British comedy troupe Monty Python. So remember, always expect the Spanish Inquisition when you're debugging!



The Zen of Python

 

If Python was a dude, it'd be the 'chill' one at the party. It's got this mantra—The Zen of Python—which is basically the 'Hakuna Matata' for coders. It whispers sweet nothings like "beautiful is better than ugly" and "simple is better than complex." Want a piece of that Zen? Just type

import this

into your Python console and get ready for some programming enlightenment.



Release the Pythons!

 

Eyebrows hit the ceiling in 2008 when Python 3 sauntered into the scene. Codenamed "Python 3000" or the cooler-sounding "Py3k", this bad boy was no mere update—it was like Python had drunk a whole new type of coffee. It had impressive new features, but also broke backwards compatibility, meaning code written in Python 2 needed to shape up or ship out. It sparked a love-hate relationship that has kept forums buzzing and devs chugging energy drinks into the wee hours.

What is the difference between Junior, Middle, Senior and Expert Python developer?



Seniority NameYears of ExperienceAverage Salary (USD/year)Responsibilities & Activities
Junior0-2$50,000 - $70,000

    • Writing simple scripts and automation tasks

    • Debugging and fixing minor bugs

    • Learning codebase and contributing to documentation

    • Assisting in code reviews with supervision


Middle2-5$70,000 - $95,000

    • Developing features with moderate guidance

    • Improvement and refactoring of code

    • Writing unit and integration tests

    • Participating in code reviews


Senior5+$95,000 - $120,000

    • Architecting and designing complex systems

    • Mentoring junior and middle developers

    • Leading technical discussions and making decisions

    • Optimizing performance and ensuring code quality


Expert/Team Lead8+$120,000+

    • Setting technical direction and strategy for teams

    • Coordinating with stakeholders on product vision

    • Overseeing project management and delivery

    • Handling complex project negotiations and risks


 

Top 10 Python Related Tech



    • Python


      Python slithers its way to the top of the list, being the charming and easy-to-read language that woos developers of all levels. Renowned for its clean syntax and powerful libraries, it's like the Swiss Army knife in a techie's toolkit. It's the VIP pass to a plethora of frameworks, tools, and libraries. Python's versatile nature lets it code everything from a tiny script to a full-fledged spaceship (okay, maybe not a spaceship).


      def greet(world):
      print(f"Hello, {world}!")
      greet("Developers")

 

    • Django


      Picture Django as the cool kid on the block that lets you whip up web applications without breaking a sweat. This high-level Python web framework follows the "batteries-included" philosophy, which means it gives you everything and the kitchen sink to avoid the dreaded "NotImplementedYet" blues.


      from django.http import HttpResponse

      def hello(request):
      return HttpResponse("Look ma! I built a web app with Django!")

 

    • Flask


      Flask is your minimalist buddy in the Python web framework world, who is a fan of simplicity and elegance. If Django is a Swiss Army knife, Flask is your trusty scalpel — precise and perfect for smaller incisions into the web dev body. It gives you the foundation to build basic web services quicker than you can say "micro-framework."


      from flask import Flask
      app = Flask(__name__)

      @app.route("/")
      def home():
      return "Flask makes web dev fun!"

 

    • NumPy


      NumPy is like the gym for Python where data goes to get buff. It's all about handling those heavy-lifting numerical operations with its powerful array objects. Data scientists and engineers flex their coding muscles with NumPy to crunch numbers faster than a calculator on a sugar rush.


      import numpy as np

      a = np.array([1, 2, 3])
      print(f"NumPy says hi: {a}")

 

    • Pandas


      Pandas is not your everyday black and white bear. In the Python jungle, it's the go-to data manipulation expert, ideal for munging and messing around with data frames. Its ability to devour messy data and spit out clean results is legendary among data wranglers and analysts.


      import pandas as pd

      df = pd.DataFrame({'A': [1, 2, 3]})
      print("Pandas and chill: ")
      print(df)

 

    • Git


      Git is the timeless classic of version control systems. It's like that trusty old spellbook for developers, keeping all versions of their magical codes safe and sound. The incantation "git commit" is often followed by a sigh of relief, knowing that changes are tucked away in their repository repository, safe from accidental catastrophes.

 

    • Docker


      Docker is the sorcerer's stone of consistent software deployment — converting applications to portable, containerized spells that can run almost anywhere. With Docker, you can stop saying, "But it works on my machine!" and start shipping apps in their cozy little environments.

 

    • PostgreSQL


      PostgreSQL, affectionately called Postgres, is the database giant that won't give you a "sql-ache". It's an open-source relational database that juggles SQL compliance with, throwing in enough advanced features that you'd think it’s doing data magic.

 

    • Redis


      Redis is like that flash memory card that surprises you with its speed every time. It's an in-memory data structure store, used as a database, cache, and message broker. It’s like giving your data a triple espresso shot, so your app's data-fetching game is always on point.

    • AWS


      AWS, or Amazon Web Services, is the colossal cloud playground where developers deploy their apps without ever worrying about running out of sandbox space. It's a haven of scalable resources, with enough services to make any developer feel like a kid in a candy store, or rather, a techie in a tech store.

 

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