Position: Data Engineer
Category: Data Engineer
English level: fluent
Years of experience: 2.5


* December 2018 – February 2019: SDE Intern at Samsung R&D Institute Ukraine.
	* Developed and modified cryptography algorithms.
	* Developed applications for steganography and steganalysis.
	* Evaluated performances of different approaches and algorithms.
* December 2019 – March 2020: Junior Security developer at Samsung R&D Institute Ukraine.
	* Developed key-logger applications for PC and smartphone.
	* Created models for user authentication/verification with data collected from those apps.
	* Evaluated performances of different models.
* June 2020 – February 2021: ML Engineer/Backend Developer at Adoriasoft Inc. 
	* Developed ML algorithms for image and video watermarking.
	* Implemented and tested multiple cutting-edge approaches and models.
	* Evaluated costs of product in production and optimized overall performance of product.
	* Deployed models to production and created APIs for serving deployed models.
* September 2020 – December 2020: part-time Data Scientist at Artellence.
	* Collected needed user-data from social networks.
	* Used collected data in e-commerce and credit-scoring models.
	* Used user clustering and segmentation for internet-marketing models.
	* Worked with big amounts(100+ million users) of client data.
* February 2021 – Now: ML/Data Engineer at Genesis.
	* Led team of 2 data engineers/data analysts.
	* Developed complex ML API’s and infrastructure using Python and AWS/GCP.
	* Developed ML models for photo/video editing and personalisation that were core features of our app → app grew from 0$ to 200k$ MRR in just 2 month after first build. 
	* Introduced Airflow as core of any data pipelines on project, optimized SQL scripts and tables → reduced infrastructure costs from 10k$/month to 3k$/month.
	* Created and led AB-test evaluation automation → saved our team 20 hours/week.
	* Integrated off-site LTV calculation and marketing automation service → saved 10k$/month in marketing costs.

* Tech Stack:
	* Languages: Python, JS.
	* Cloud Computing Providers: AWS, GCP.
	* VSC skills: Gitlab, Github, CI/CD.
	* AWS Stack: ECS, EC2, ECR, Lambda, S3, Redshift.
	* GCP Stack: GCS, GCR, GKE, Cloud Functions, Big Query, Cloud Composer, Compute Engine, Dataproc.
	* Databases: BigQuery, Postgres, Mongo, Redshift.
	* Analytics and Data Packages: Airflow, Scipy, Numpy, Pandas, Pyspark, GCP and AWS libs for python.
	* Analytics Software: Appsflyer, RevenueCat, Amplitude, Firebase(Google Analytics), Algolift.