Wojciech Kulikowski, Software Developer in Berlin, Germany
Wojciech Kulikowski

Software Developer in Berlin, Germany

Member since May 18, 2020
Wojciech is a senior engineer with four years of experience in full-stack software development. His main tools are Python and React. A productivity and efficiency advocate, he always looks for optimal solutions for both developer- and user- experience. He worked with clients, ranging from fast-growing SF startups to F500 companies, helping all of them achieve both business and tech results.
Wojciech is now available for hire


  • Toptal Clients
    Django, React, Facebook API, JavaScript, Python
  • Self Made
    Django REST Framework, Django ORM, APIs, React, PostgreSQL...
  • 10Clouds
    Django REST Framework, React, JavaScript, Django ORM, APIs, PostgreSQL...



Berlin, Germany



Preferred Environment

MacOS, Linux, Zoom, Slack, VS Code, GitHub, Git

The most amazing...

...project I worked on is a social network for underrepresented groups in venture capital. It enabled me to work at a high-growth startup and make a direct impact.


  • Senior Software Developer

    2020 - PRESENT
    Toptal Clients
    • Developed a full Facebook Marketing API integration for an ad management app. The API integration includes logins with Facebook and the marketing API.
    • Built a migration from a monolith Django back end to a serverless system for a top US research nonprofit organization.
    • Worked with a San Francisco-based social network startup. Within a legacy environment, built a "forum" feature within a legacy environment, wrote extensive documentation, onboarded new engineers, and helped design the new CI/CD architecture.
    Technologies: Django, React, Facebook API, JavaScript, Python
  • Founder

    2020 - PRESENT
    Self Made
    • Built and sold a logistics solution for a german lumber company. The system involved a mobile app for drivers, a mobile app for barcode scanners, an admin back end for data management, and integration with the client's ERP system.
    • Built an analytics system for a French medtech startup. We tracked their marketing performance and orders. Our solution helped them grow by over 120% year over year and, to this day, is used as the main source of analytics.
    • Built a marketplace for imported cars from Switzerland. The project integrated with four insurance companies to provide a data stream of cars in our marketplace.
    • Managed a team of three other developers, set up our cloud deployments and CI pipelines, and worked directly with our clients and their business needs for most of our projects.
    Technologies: Django REST Framework, Django ORM, APIs, React, PostgreSQL, Google Cloud Platform (GCP), JavaScript, Facebook API, Amazon Web Services (AWS), Linux, Django, Python, AWS, Kubernetes, Serverless, Next.js
  • Software Engineer

    2019 - 2020
    • Handled the end-to-end development of a chatbot management system. Users (recruiters) could create chatbots, deploy them to Facebook Messenger, and connect to candidates through Facebook Ads (Findem.online).
    • Built and maintained OnBoard, a KYC AML solution for fintech solutions. Worked in a highly restrictive space with many privacy and security requirements (Onboard.truststamp.ai).
    • Built the back end for a marketplace for creative services (Publicist.co).
    • Built the back end for a marketplace for matching and management platform for building and renovation projects (Ibuildpro.com).
    • Wrote a blog post for the company blog about simplifying your Python development process (10clouds.com/blog/python-tools-to-radically-simplify-your-development).
    • Managed and hosted 14 internal knowledge-sharing sessions for over 30 participants each. We discussed cutting-edge topics like Kubernetes, asynchronous Python, and serverless architecture.
    Technologies: Django REST Framework, React, JavaScript, Django ORM, APIs, PostgreSQL, Docker, Facebook API, Stripe, Amazon Web Services (AWS), Linux, Django, Python, Kubernetes, AWS
  • Software Engineer

    2018 - 2019
    • Developed a custom MTurk management system for labeling a computer vision dataset. Users could annotate the pictures for a small price and we could annotate thousands of pictures this way without a problem.
    • Deployed an NLP recommendation engine for optimizing Amazon product descriptions.
    • Led a product prototype phase for GovTech—a technological contest for Polish companies building the future of public solutions.
    • Built an NLP based Google AdWords management system and integrated it as a Slack application.
    Technologies: Django REST Framework, JavaScript, PostgreSQL, Docker, Google Cloud Platform (GCP), Linux, Django, Python, Kubernetes, Flask
  • Data Engineer

    2018 - 2018
    • Worked within a data science team and took care of internal ETL processes.
    • Improved the fake-user detection algorithm (100% more trials, sales, and single time users excluded from analysis).
    • Optimized queries on datasets with more than 30 million records.
    Technologies: Amazon Web Services (AWS), APIs, PostgreSQL, Linux, Python, Bash, MySQL, Redshift, AWS


  • NU4IT Ad Manager


    The client was a company specializing in robotic process automation. For this project, NU4IT needed support with web development, marketing, and Facebook.

    Project Description

    From the beginning, the project had a clearly specified goal—to develop a web app that allows Facebook marketers to rapidly experiment with their Facebook Ads parameters.

    The users should be able to:

    - Login with Facebook and log out
    - See and review their ad campaigns, ad sets, and ads
    - Create a new campaign
    - Create a config that defines multiple ad sets at once, publishes them, and allows them to monitor which ones perform the best
    - Create an ad for those ad sets and run the experiment
    - Monitor experiment


    We delivered a working MVP after one month of development. Every user story has been successfully delivered, and the app has successfully launched to over 200 users.

    In MVP, all ads are image ads, and users can define their title, message, image, and call to action.

    After the whole campaign has been created, users can analyze the performance in the ad tracking tab.

    Technology used

    - Python
    - Django
    - Facebook Marketing API

  • OnBoard

    A KYC AML solution to integrate with fintech solutions. Authenticating users are going through the following steps:
    1. Basic information input
    2. Face scan/liveness check
    3. Front and back ID scan
    4. Face match from photos and the video
    5. Check public watchlists

    The back end was developed with Django and deployed with Kubernetes on AWS.

  • FindEm

    For a German client, we built a complex recruitment tool. A recruiter could generate a job posting and a chatbot that performs the initial candidate screening. Then, with only one click, the posting is being deployed as a targeted ad on Facebook, alongside the corresponding messenger chatbot.
    I was responsible for building the chatbot system. It has been developed as a separate service and put under the biggest load when the app went to production.

  • iKnowA

    A marketplace connecting homeowners with renovators. The back end was developed in Django and exposed as a REST API to a React front end. We hosted the solution on AWS ECS, an Amazon container orchestration tool.

  • Publicist

    Publicist is a marketplace connecting PR specialists and companies in need of PR services.
    We built the project with Django, React, and Stripe. I was responsible for developing the back end specifically for the project management flow and payments.

  • Sales and Marketing Analytics System for A Medtech Startup

    The system had four responsibilities:
    - It should collect historical data back to 2018
    - It should run in hourly intervals to collect up-to-date orders from Amazon and Shopify
    - It should run in hourly intervals to collect up-to-date ad spending from Facebook, Google, and Amazon
    - It should calculate the profit margins for every product (with regard to platform commissions, taxes, and shipping costs)

    The project has been hand over within a month. It correctly collected all the historical data, running for over 24 hours due to Amazon & Shopify rate limits.

    From this point, the data collection process runs as an hourly cron job.

    - After completing the project using Amazon MWS, was released. Amazon Selling Partner API would be more effective but it had not yet been released.
    - It was difficult to acquire Shopify fee data. Finally, we succeeded by looking at the payment receipt info. It was not documented well and we had to dig into many pages of stack overflow and Shopify community answers.

    Technology used:
    - Python
    - Google Sheets API
    - Amazon MWS
    - Shopify API
    - Facebook Marketing API
    - Google Ads API
    - Amazon Advertising API


  • Languages

    Python, JavaScript, SQL, HTML, GraphQL, Bash, Solidity, Rust
  • Frameworks

    Django, Django REST Framework, Next.js, Flask, OAuth 2
  • Libraries/APIs

    Facebook API, React, Stripe, Slack API, Django ORM, Amazon MWS, Shopify API, Google Sheets API, Facebook Marketing API, Facebook Ads API, Facebook Login, Google Ads API, Amazon Product Advertising API
  • Other

    APIs, Serverless, Software Architecture, AWS
  • Platforms

    Linux, Amazon Web Services (AWS), Google Cloud Platform (GCP), Docker, Kubernetes, Shopify, Heroku, MacOS, Ethereum
  • Storage

    PostgreSQL, AWS DynamoDB, Redshift, MySQL
  • Tools

    Terraform, Jenkins, Git, GitHub, VS Code, Slack, Zoom, Google Sheets, Celery

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