Staff Data & Platform Engineer | 10+ years professional experience (since 2016)

Strongly technical leader with proven expertise in designing and evolving enterprise-wide data and ML/AI platforms. Track record of driving cross-team collaboration, establishing architectural standards, and making strategic technical decisions that balance business needs with long-term scalability and operational excellence.

Built company-wide systems from scratch, including:

• Data platforms and data lakes using Delta Lake, Apache Iceberg, and Databricks
• Agentic and LLM platforms for enterprise-scale AI use cases
• Data Science development platforms supporting end-to-end ML and GenAI workflows
• Full MLOps lifecycle: CI/CD pipelines, monitoring (Datadog), A/B testing infrastructure on AWS (Lambda, ECS, ECR, DynamoDB)
• Infrastructure provisioned with Pulumi and Terraform (IaC)

Core expertise:

• GenAI, LLMs, RAGs, Coding agents platforms (AWS Agent Core, LangGraph, Bedrock, Azure OpenAI, Opencode, Claude code etc.)
• Apache Spark (batch & streaming ingestion, performance optimization), Delta Lake, Apache Iceberg, data catalogs
• Databricks, AWS ecosystem, ML/DevOps, CI/CD
• Software architectures: distributed systems, concurrent programming, event-driven architectures
• Languages: Python (primary), Scala; functional programming principles applied throughout

Strong advocate for software testing and framework design.

Dedicated to continuous learning through in-depth technical literature.

Maintaining multiple OSS projects, including:

• pramen-py https://github.com/AbsaOSS/pramen/tree/main/pramen-py - A framework enabling convenient definition of Spark transformation pipelines.

• pytest-when https://github.com/zhukovgreen/pytest-when - A project helping BDD testing in a very convenient way

• friendly-sequences https://github.com/zhukovgreen/friendly-sequences - A small but powerful type-safe library for function chaining.

• Talks repository - https://github.com/ZhukovGreen/talks (contains many educational sessions about data lakes, python and testing)
• PyCon CZ 2023 - Can we have a better feature store https://cz.pycon.org/2023/program/talks/65/
• DevConf.US 2021 - Framework for integration tests lifecycle https://www.youtube.com/watch?v=K7VcLnHRz0w&list=PLU1vS0speL2ZbTPg-aU2Rw2s6IPsTVoCF&index=60
• PyAmsterdam 2020 - Using asyncio for building CLI applications https://py.amsterdam/2020/10/15/virtual-pyamsterdamnowtzzoneinfoeuropeamsterdam-stayathome.html
• PyCon CZ 2018 - HVAC engineer and Python https://youtu.be/KAZn2Fhh7f4?t=324

Dates Employed Feb 2025 - present

Building various infrastructure projects around LLMs Mentoring the team in the software architectures principles Solving complex design changes and developing the architecture of new systems

Dates Employed Aug 2022 - present

Building data platform for data scientists.

Databricks on AWS is the main platform. Pulumi / Terraform for infrastructure. Python is the primary language. Spark and Delta Lake.

Key responsibilities:

• Setting standards for team software development practices
• Building data platform
• Enabling data quality system
• Developing data transformation framework with metadata driven schema evolution and many other nice features
• Automating Databricks pipeline deployment
• Managing feature store, data contracts, and table metadata
• Optimizing data transformations and debugging spark jobs at scale
• Processing streaming and batch data
• Working with big data, Delta Lakes, data ingestion, and data catalogs

Dates Employed Aug 2021 - Aug 2022

Developed and built components of an on-premise system for convenient big data ETL processes, with abstractions around the data warehouse for data scientists (feature-centric interfaces). Also developed various data transformations for different projects and maintained an existing project.

Dates Employed Jul 2021 - Aug 2021

Worked on the Convert2RHEL team. Designed a simple but specific and rich CI system for developing and running integration tests (libvirt, Ansible, Testing Farm, tmt). Developed new features in the upstream project, performed code reviews, adopted pytest, transitioned the app to Python 3, and provided mentoring.

A note from the promotion document:

Artem joined Red Hat during the spring of 2020 as a software developer working on
the LEAPP team. Artem’s enthusiasm for Python and pythonic development practices
soon led him to adopt an advocacy role on his team.
Artem transitioned to the Convert2RHEL team in early 2021, and rapidly became one
of the team’s most prolific contributors. He continued to broaden his reach beyond
his SST by creating a hardware deprecation database and associated microservice
which helps to take the mystery out of hardware support.
Artem’s creativity and energy have made him a true asset to his teams.

Dates Employed Apr 2020 - June 2021

Worked on the OS & App Modernization team (OAMG).

Primary responsibilities:

• Maintained and contributed to OAMG repositories https://github.com/oamg
• Developed a data delivery system (internal framework to distribute various data to clients)
• Worked on convert2rhel utility (new features and CI)

Dates Employed Jan 2016 – Jan 2020

Building a software platform to support new products and company processes.

Dates Employed May 2006 – Aug 2016

Worked in various positions within the HVAC industry:

• Compact Air Handling Units (AHU) project manager (~1 year)
• AHU technical support (~1 year)
• HVAC designer (~5 years)
• Energy modeler for LEED certification (~1 year)
• Technical supervisor on site (~1 year)
• Ventilation systems installer (~1 year)

CS229: Machine Learning Dates: 2016 – 2017

Completed all lecture videos and keynotes, resolved all assignments. Course syllabus: http://cs229.stanford.edu/syllabus.html

Machine Learning Engineer Nanodegree Dates: 2016 – 2018

https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

Degree Name Master’s Degree Field Of Study Mechanical Engineering (HVAC) Grade M.Sc. in heating, ventilation, air conditioning systems Dates attended or expected graduation 2002 – 2008

This is my primary base education. A lot of mathematics, physics, and drawings.

• Udacity: PyTorch Scholarship Challenge from Facebook
• AWS trainings (Big Data, Data Lakes, Developing with AWS)
• A vast amount of different courses at Udemy/Coursera, such as data structures and algorithms, functional programming, PyTorch Reinforcement learning, etc.

• Russian - native
• English - good professional level
• Czech - good professional level