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Anatolii K., Python Software Engineer

Kamianets-Podilskyi, Ukraine
Last Updated: 5 Apr 2024
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- 4 years of experience in the IT industry as a Python Engineer - Proficient in using technologies such as Django DRF, Flask, Pandas, BeautifulSoup, SQLAlchemy, Asyncio, Flask + Marshmallow, Apscheduler, Jinja, Quip API, Docker, Nginx, Amazon MWS API, Amazon SP-API, Google API, and Telegram API; - Strong in refactoring, bug-fixing, and working with Python, JavaScript, Django, Django Rest Framework; - Skilled in developing faceted search and matching algorithms using ElasticSearch; - Experienced in PDF generation and importing from various formats; - Developed web applications, API interfaces, and automated scripts for data transformation; - Previous experience includes working as a Python developer focused on data scraping for Amazon, eBay, and Walmart products in dropshipping projects; - Also worked as a C# developer on various projects, specializing in web development, scraping, and parsing data; - Upper-Intermediate English; - Available ASAP

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Vlad D, Full-Stack Python Software Engineer

Kyiv, Ukraine
Last Updated: 4 Jul 2023
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- Full-Stack (Python + Vue.js) Software Engineer with a background in digital marketing; - Core Python, Flask & Django Frameworks, reusable code - RESTful APIs - MVC, SOLID, DRY principles; - SQL, Django ORM, Elastic Search Platform - Upper-intermediate English; - Available ASAP

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Nickson M., Senior Python/Golang Engineer

Kampala, Uganda
Last Updated: 4 Jul 2023
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- 6 years of experience in IT - 5 years of experience with Golang - 6 years of experience with Python - Fluent English - Available ASAP

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Python   6 yr.


Go   5 yr.

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Olha K., Python Engineer, Data Quality, ML

Radomsko, Poland
Last Updated: 4 Jul 2023
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- 13 years of experience in IT - Proficiency in Python, Pandas. - Data quality for ICC profiles and algorithms for display color calibration - Mathematical modeling (MATLAB, Octave, Comsol, MathCad), software algorithms, numerical methods (algebra, interpolation, regression, nonlinear problems, optimization, ordinary and partial differential equations), machine learning. - Upper-Intermediate English. - Available in two weeks

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Henry A., Software Engineer with Python and Data Analytical Skills

Last Updated: 14 Mar 2024
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- 8+ years experience working with Python; - 5 years of experience as a BI and 4 years of experience with Tableau; - 8 years of experience with various data sets (ETL, Data Engineer, Data Quality Engineer); - 3 years of experience with Amazon Web Services (AWS), Google Cloud Platform (GCP); - Data Analytics/Engineering with Cloud Service Providers (AWS, GCP) - Experience working with MySQL, SQL, and PostgreSQL; - Deep abilities working with Kubernetes (K8s); - Hands-on scripting experience with Python; Microsoft Power BI, Tableau, Sisense, CI/CD principles, Data Validation, Data QA, SQL, Pipelines, ETL, and Automated web scraping. - Pet web3 projects (solidity, wallet integration) - Upper-intermediate English

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Python   8.5 yr.

Data Analysis

Data Analysis   6 yr.

Google Cloud Platform (GCP)

Google Cloud Platform (GCP)   4 yr.


Tableau   4 yr.

Microsoft Power BI

Microsoft Power BI   4 yr.

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Table of Contents

Pros & cons of Python

8 Pros of Python

  • Easy to Read and Write: Python has a clean and straightforward syntax, making it easy to understand and write code. It emphasizes readability, which reduces the cost of program maintenance.
  • Large Standard Library: Python comes with a vast standard library that provides ready-to-use functions and modules for various tasks, such as file handling, networking, and web development. This extensive library helps developers save time and effort.
  • Platform Independent: Python is an interpreted language, which means it can run on different operating systems such as Windows, macOS, Linux, and UNIX. This portability allows developers to write code once and run it anywhere without making significant changes.
  • Great for Prototyping and Rapid Development: Python’s simplicity and ease of use make it an ideal choice for prototyping and rapid development. Its extensive ecosystem of frameworks and libraries, such as Django and Flask, further accelerates the development process.
  • Strong Community Support: Python has a large and active community of developers who contribute to its growth and provide support through forums, documentation, and online resources. This community-driven aspect ensures that developers can easily find solutions to their problems.
  • Excellent for Data Analysis and Scientific Computing: Python offers powerful libraries like NumPy, Pandas, and Matplotlib, which are widely used for data analysis, scientific computing, and visualization. These tools make Python a popular choice among data scientists and researchers.
  • Integration Capabilities: Python can seamlessly integrate with other programming languages such as C, C++, and Java. This feature allows developers to leverage existing code and libraries written in other languages, enhancing code reusability and flexibility.
  • Highly Extensible: Python supports the integration of new modules and libraries, allowing developers to extend its functionality. The ability to write Python extensions in languages like C or C++ makes it a versatile language for building complex applications.

8 Cons of Python

  • Slower Execution Speed: Compared to languages like C or Java, Python can be slower in terms of execution speed. This is due to its interpreted nature, which requires an interpreter to execute the code line by line. However, for most applications, the difference in speed is negligible.
  • Global Interpreter Lock (GIL): Python’s Global Interpreter Lock ensures that only one thread executes Python bytecode at a time. This can limit the performance of multi-threaded applications that heavily rely on CPU-bound tasks. However, Python provides multiprocessing libraries to overcome this limitation.
  • Not Ideal for Mobile Development: While Python has frameworks like Kivy and BeeWare for mobile app development, it is not considered the best choice for this purpose. Native app development for platforms like iOS and Android is typically done in languages like Swift, Objective-C, Java, or Kotlin.
  • Design Restrictions: Python’s simplicity can sometimes limit the design choices available to developers. The language’s design philosophy prioritizes readability and simplicity over more advanced language features found in languages like C++ or Rust.
  • Python 2.x Legacy: Python 2.x, which is incompatible with Python 3.x, was widely used in the past. While Python 3.x has been around for over a decade, some legacy codebases still rely on Python 2.x. However, efforts are being made to migrate to Python 3.x.
  • Not Well-suited for Memory-intensive Tasks: Python’s memory management system may not be ideal for memory-intensive tasks. The language relies on automatic garbage collection, which can lead to occasional performance issues in scenarios that require fine-grained memory control.
  • Less Suitable for High-performance Computing: Python’s interpreted nature and high-level abstractions make it less suitable for high-performance computing tasks that require low-level control and maximum efficiency. Languages like C or Fortran are better suited for such scenarios.
  • Dependency Management Challenges: Python’s dependency management can sometimes be challenging, especially when dealing with complex projects with multiple dependencies. However, tools like pip and virtual environments mitigate this issue to a large extent.


What are top Python instruments and tools?

  • PyCharm: PyCharm is a powerful integrated development environment (IDE) specifically designed for Python. Developed by JetBrains, PyCharm provides a wide range of features such as code completion, debugging, version control integration, and support for web development frameworks like Django and Flask. It was first released in 2010 and has since gained popularity among Python developers due to its user-friendly interface and extensive functionality.
  • Jupyter Notebook: Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and explanatory text. Originally known as IPython Notebook, it was introduced in 2011 as a part of the IPython project. Jupyter Notebook supports over 40 programming languages, including Python, and has become a popular choice for data analysis, scientific computing, and machine learning tasks.
  • TensorFlow: TensorFlow is an open-source machine learning framework developed by Google Brain. It was initially released in 2015 and has gained widespread adoption in both academia and industry. TensorFlow provides a comprehensive ecosystem for building and deploying machine learning models, offering features such as automatic differentiation, distributed computing, and support for both CPU and GPU acceleration. Its flexibility and scalability have made it a go-to tool for tasks ranging from natural language processing to computer vision.
  • Pandas: Pandas is a Python library for data manipulation and analysis. It was created by Wes McKinney and first released in 2008. Pandas provides data structures and functions that enable efficient handling and processing of structured data, such as CSV files or SQL tables. With its intuitive syntax and powerful capabilities for data cleaning, transformation, and aggregation, Pandas has become a staple tool for data scientists and analysts in various domains.
  • NumPy: NumPy is a fundamental library for numerical computing in Python. It was initially developed by Travis Oliphant in 2005 as an extension to the Numeric library. NumPy introduces powerful data structures and functions for handling large, multi-dimensional arrays and matrices, along with a vast collection of mathematical operations. Its efficiency and versatility have made it a cornerstone of scientific computing and essential for tasks like numerical simulations, signal processing, and linear algebra.
  • Django: Django is a high-level Python web framework that follows the model-view-controller (MVC) architectural pattern. It was created by Adrian Holovaty and Simon Willison in 2003 and has since become one of the most popular choices for web development in Python. Django provides a robust set of tools and features for building scalable and secure web applications, including an ORM (Object-Relational Mapping) for database management, a templating engine, and support for user authentication and authorization.
  • Flask: Flask is a lightweight web framework for Python, designed to be simple and easy to use. It was developed by Armin Ronacher and first released in 2010. Flask is known for its minimalistic approach, allowing developers to have more control over the application structure and customization. Despite its simplicity, Flask offers all the essential features needed for web development, such as URL routing, request handling, and template rendering. It has gained popularity for building small to medium-sized web applications and APIs.
  • Matplotlib: Matplotlib is a plotting library for Python widely used for creating static, animated, and interactive visualizations. It was originally created by John D. Hunter in 2003 and has since become a standard tool for data visualization in Python. Matplotlib provides a wide range of plotting functions and customization options, allowing users to create various types of charts, graphs, and plots. Its versatility and integration with other libraries make it a valuable asset for scientists, engineers, and data analysts.


TOP 11 Tech facts and history of creation and versions about Python Development

  • Python is a high-level programming language created by Guido van Rossum in 1991. It is known for its simplicity and readability, making it an ideal language for beginners.
  • The name “Python” was inspired by the British comedy group Monty Python, not the snake. This reflects the language’s playful and quirky nature.
  • Python follows the philosophy of “batteries included,” which means it comes with a comprehensive standard library that provides pre-built modules for various tasks.
  • In 2008, Python 3 was released, introducing several significant changes and improvements. However, due to compatibility issues, Python 2 continued to be used for many years.
  • Python is an interpreted language, meaning that it does not require compilation before execution. This allows for rapid development and easy debugging.
  • Python’s indentation-based syntax, using whitespace to delimit code blocks, is seen by some as controversial but has been praised for its readability and enforcing consistent formatting.
  • Python has a strong and active community of developers who contribute to its open-source ecosystem. This has led to the creation of numerous third-party libraries and frameworks.
  • Python is widely used in various domains, including web development, data analysis, artificial intelligence, scientific computing, and automation.
  • Python’s popularity has been steadily increasing over the years. In the TIOBE Index, which ranks programming languages by popularity, Python consistently ranks among the top few.
  • Python is the programming language of choice for many tech giants, including Google, Facebook, Instagram, and Netflix. Its versatility and ease of use make it suitable for a wide range of applications.
  • Python’s community-driven development process ensures that the language continues to evolve and adapt to new technologies and trends, making it a future-proof choice for developers.


How and where is Python used?

Case NameCase Description
Web DevelopmentPython is widely used in web development due to its simplicity and versatility. It offers numerous frameworks such as Django and Flask, which facilitate the creation of scalable and robust web applications. Python’s extensive library ecosystem also provides modules for handling tasks like URL routing, database connectivity, and template rendering.
Data AnalysisPython is the go-to language for data analysis and scientific computing. With libraries like NumPy, Pandas, and Matplotlib, Python enables efficient data manipulation, exploration, and visualization. These tools make it easier to extract valuable insights from large datasets, perform statistical analysis, and create informative visualizations.
Machine LearningPython is widely used in the field of machine learning due to its simplicity, ease of use, and the availability of powerful libraries such as TensorFlow, Keras, and scikit-learn. These libraries provide a wide range of tools and algorithms for tasks like classification, regression, clustering, and neural networks, making Python a preferred choice for building and deploying machine learning models.
Scripting and AutomationPython’s simplicity and readability make it an ideal language for scripting and automation tasks. It allows developers to automate repetitive tasks, manage system resources, and interact with operating systems through modules like os, subprocess, and shutil. Python’s scripting capabilities are utilized in various domains, including system administration, network automation, and task scheduling.
Game DevelopmentPython’s versatility extends to game development as well. Popular game development frameworks and libraries like Pygame and Panda3D provide the necessary tools and resources for creating games. Python’s simplicity and easy syntax make it an attractive choice for both beginners and experienced developers looking to develop interactive and engaging games.
Internet of Things (IoT)Python is widely used in IoT applications due to its simplicity and compatibility with various hardware platforms. Python libraries like Raspberry Pi GPIO and PySerial enable developers to interact with sensors, control actuators, and build IoT applications. Python’s ease of use and rapid prototyping capabilities make it a popular choice in the IoT space.
Desktop GUI ApplicationsPython provides several frameworks, such as PyQt and Tkinter, for developing desktop GUI applications. These frameworks offer a wide range of graphical elements and tools for creating user-friendly interfaces. Python’s cross-platform compatibility allows developers to build applications that can run on different operating systems without major modifications.
Web ScrapingPython’s rich ecosystem includes libraries like Beautiful Soup and Scrapy, which are widely used for web scraping. These libraries enable developers to extract data from websites, parse HTML and XML documents, and automate data extraction tasks. Python’s simplicity and powerful web scraping capabilities make it a popular choice for data mining and information gathering.
Image ProcessingPython offers powerful libraries such as OpenCV and PIL (Python Imaging Library) for image processing tasks. These libraries provide a wide range of functionalities for tasks like image manipulation, filtering, object detection, and computer vision. Python’s ease of use and extensive library support make it a preferred choice for various image processing applications.
Natural Language Processing (NLP)Python is widely used in natural language processing tasks, such as text analysis, sentiment analysis, and language translation. Libraries like NLTK (Natural Language Toolkit) and SpaCy provide tools and algorithms for processing and analyzing textual data. Python’s simplicity and the availability of pre-trained models make it a popular choice for NLP applications.


Cases when Python does not work

  1. Performance-intensive tasks: While Python is known for its simplicity and ease of use, it may not be the best choice for performance-intensive tasks. Python is an interpreted language, which means that it is not as fast as compiled languages like C or Java. If you are working on computationally heavy tasks such as complex mathematical calculations or real-time data processing, Python might not provide the desired performance.
  2. Mobile app development: Python is not commonly used for mobile app development. While there are frameworks like Kivy and BeeWare that allow you to build cross-platform mobile apps using Python, the majority of mobile app developers prefer languages like Java or Swift, which are more optimized for mobile platforms.
  3. System-level programming: Python may not be the most suitable language for system-level programming, such as developing operating systems or device drivers. These types of tasks often require low-level control over hardware, which is better achieved with languages like C or assembly language.
  4. Embedded systems: Python is not commonly used for developing software for embedded systems, which are small computer systems embedded within larger devices. Embedded systems often have limited resources and require programming languages with low memory footprint and efficient resource utilization. Languages like C and C++ are more commonly used in this domain.
  5. Real-time applications: Python may not be the best choice for developing real-time applications that require immediate response and low-latency. Due to its interpreted nature and garbage collection mechanism, Python may introduce some latency and unpredictability in time-critical scenarios. Real-time systems often rely on languages like C or C++ for precise control and deterministic behavior.


Soft skills of a Python Developer

Soft skills are essential for Python developers as they contribute to effective collaboration, communication, and problem-solving. Here are the soft skills required for developers at different levels:


  • Adaptability: Ability to quickly learn and adapt to new technologies and programming languages.
  • Teamwork: Capability to work collaboratively with other team members and contribute to group projects.
  • Communication: Strong verbal and written communication skills to effectively express ideas and collaborate with team members.
  • Time Management: Efficiently managing time and meeting project deadlines.
  • Problem-solving: Aptitude for analyzing and finding solutions to programming challenges.


  • Leadership: Ability to take initiative, guide junior developers, and provide technical mentorship.
  • Critical Thinking: Proficiency in evaluating complex problems and devising innovative solutions.
  • Project Management: Experience in managing projects, setting priorities, and coordinating with stakeholders.
  • Collaboration: Aptitude for working seamlessly with cross-functional teams and stakeholders.
  • Empathy: Understanding the perspectives and needs of team members and stakeholders.
  • Attention to Detail: Diligence in ensuring accuracy and precision in coding and project deliverables.
  • Continuous Learning: Willingness to stay updated with the latest trends and advancements in Python and related technologies.


  • Strategic Thinking: Ability to align technical decisions with broader business goals and objectives.
  • Conflict Resolution: Skill in resolving conflicts and fostering a positive team environment.
  • Mentorship: Experience in mentoring junior and middle developers and helping them grow.
  • Client Management: Proficiency in understanding client requirements and effectively managing client relationships.
  • Decision Making: Capability to make informed and timely decisions in complex technical situations.
  • Presentation Skills: Proficiency in presenting technical concepts and solutions to stakeholders.
  • Innovation: Aptitude for driving innovation and exploring new approaches in Python development.
  • Quality Assurance: Attention to quality assurance practices and ensuring code reliability and maintainability.

Expert/Team Lead

  • Strategic Leadership: Ability to provide vision, drive technical strategies, and lead a team towards success.
  • Project Planning: Proficiency in planning and executing large-scale projects with multiple stakeholders.
  • Business Acumen: Understanding of business principles and ability to align technical decisions with organizational objectives.
  • Risk Management: Capability to identify and mitigate risks associated with complex development projects.
  • Team Building: Skill in building and managing high-performing development teams.
  • Client Engagement: Experience in engaging with clients at a strategic level and understanding their business needs.
  • Influence and Negotiation: Proficiency in influencing decision-making and negotiating with stakeholders.
  • In-depth Python Knowledge: Expertise in Python programming language and its advanced concepts.
  • Code Review: Proficiency in conducting code reviews and ensuring adherence to coding standards.
  • Continuous Improvement: Commitment to driving continuous improvement in development processes and practices.
  • Technical Expertise: Deep understanding and knowledge of various Python frameworks, libraries, and tools.


TOP 10 Python Related Technologies

  • Python

    Python is a popular programming language known for its simplicity and readability. It has a large and active community, making it easy to find support and resources. Python is versatile and widely used in various domains, including web development, data analysis, scientific computing, and artificial intelligence.

  • Django

    Django is a high-level Python web framework that follows the model-view-controller (MVC) architectural pattern. It provides a robust set of tools and features for building scalable and secure web applications. Django’s batteries-included approach makes it efficient and productive for developers.

  • Flask

    Flask is a lightweight and flexible Python web framework that emphasizes simplicity and minimalism. It is popular among developers who prefer a microframework that allows for greater flexibility and customization. Flask is well-suited for small to medium-sized projects and RESTful API development.

  • NumPy

    NumPy is a fundamental library for scientific computing in Python. It provides support for multi-dimensional arrays, mathematical functions, and linear algebra operations. NumPy is widely used in fields such as data analysis, machine learning, and numerical simulations.

  • Pandas

    Pandas is a powerful data manipulation and analysis library for Python. It offers data structures and functions for efficiently working with structured data, such as CSV files or SQL tables. Pandas is commonly used in data preprocessing, exploration, and transformation tasks.

  • Matplotlib

    Matplotlib is a plotting library for Python that enables the creation of various types of visualizations, including line plots, bar charts, histograms, and scatter plots. It provides extensive customization options and is widely used for data visualization and scientific plotting.

  • TensorFlow

    TensorFlow is an open-source machine learning framework developed by Google. It offers a comprehensive ecosystem for building and deploying machine learning models. TensorFlow’s extensive support for deep learning algorithms makes it a popular choice for tasks such as image recognition and natural language processing.


Hard skills of a Python Developer

As a Python Developer, it is essential to possess a range of hard skills that enable you to excel in your role. Here are the hard skills required for different levels of experience:


  • Python Fundamentals: Proficiency in core Python concepts such as variables, data types, conditionals, loops, and functions.
  • Object-Oriented Programming (OOP): Understanding of OOP principles and the ability to implement them in Python.
  • Basic Web Development: Familiarity with HTML, CSS, and JavaScript for building simple web applications.
  • Database Management: Knowledge of SQL and experience in working with databases using Python libraries like SQLAlchemy.
  • Debugging and Testing: Ability to troubleshoot and debug code issues, as well as write and execute unit tests.


  • Web Frameworks: Proficiency in popular Python web frameworks like Django or Flask for developing robust web applications.
  • API Integration: Experience in integrating external APIs into Python applications and handling data exchange.
  • Data Manipulation and Analysis: Competency in libraries like Pandas and NumPy for data manipulation, analysis, and visualization.
  • Version Control: Proficient in using Git for version control and collaborating with other developers.
  • Performance Optimization: Understanding of optimization techniques to improve the speed and efficiency of Python code.
  • Software Development Lifecycle (SDLC): Knowledge of software development processes, including requirements gathering, design, development, testing, and deployment.
  • Linux/Unix: Familiarity with Linux/Unix command-line interface and basic system administration tasks.


  • Advanced Web Development: Expertise in building complex web applications with advanced features like user authentication, RESTful APIs, and real-time updates.
  • Concurrency and Parallelism: Proficiency in handling concurrent programming and utilizing parallel computing techniques.
  • Machine Learning: Understanding of machine learning concepts and experience in implementing ML algorithms using libraries like scikit-learn or TensorFlow.
  • Big Data Processing: Knowledge of distributed computing frameworks like Apache Spark for processing large-scale datasets.
  • Cloud Computing: Experience in deploying and managing Python applications on cloud platforms like AWS, Azure, or Google Cloud.
  • Containerization: Familiarity with containerization technologies like Docker for packaging and deploying applications.
  • Code Review and Mentoring: Ability to perform code reviews, provide constructive feedback, and mentor junior developers.
  • Leadership Skills: Strong leadership qualities to guide and coordinate a team of developers in delivering high-quality software solutions.

Expert/Team Lead

  • Architectural Design: Proficiency in designing scalable and maintainable software architectures using design patterns and best practices.
  • Performance Tuning: Expertise in optimizing the performance of large-scale systems and addressing bottlenecks.
  • DevOps: Knowledge of CI/CD pipelines, automated testing, and infrastructure automation using tools like Jenkins, Ansible, or Terraform.
  • Security: Understanding of secure coding practices, vulnerability assessment, and mitigation techniques.
  • Project Management: Ability to lead and manage software development projects, including planning, resource allocation, and risk management.
  • Technical Documentation: Proficient in creating comprehensive technical documentation for projects and APIs.
  • Continuous Learning: Dedication to staying updated with the latest trends and advancements in the Python ecosystem.
  • Excellent Communication: Strong communication and collaboration skills to effectively interact with stakeholders and team members.
  • Problem Solving: Exceptional problem-solving abilities to tackle complex technical challenges and find innovative solutions.
  • Team Management: Experience in managing and motivating a team of developers, delegating tasks, and ensuring project success.
  • Business Acumen: Understanding of business requirements and the ability to align technical solutions with organizational goals.


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