Mykola S.

⚡ 1𝗠+ 𝗿𝗼𝘄𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝗱 𝗱𝗮𝗶𝗹𝘆. 30𝗞 𝗽𝗮𝗴𝗲𝘀/𝗱𝗮𝘆 𝘀𝗰𝗿𝗮𝗽𝗲𝗱 𝗮𝘁 99.95% 𝘂𝗽𝘁𝗶𝗺𝗲. 𝗥𝗲𝗽𝗼𝗿𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗰𝘂𝘁 𝗳𝗿𝗼𝗺 3 𝗵𝗼𝘂𝗿𝘀 𝘁𝗼 2 𝗺𝗶𝗻𝘂𝘁𝗲𝘀. Senior Data Engineer and Python Data Engineer with 10+ years building Python Web Scraping and Data Extraction systems, ETL/ELT pipelines, and GCP BigQuery infrastructure for businesses that need their data to work - automatically, reliably, and at scale. If your team is still collecting data manually, waiting hours for reports, or getting blocked by anti-bot systems - I build the Big Data pipelines and Web Scraping systems that eliminate that entirely. 💡 𝗖𝗼𝗿𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀: ⚙️ 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗘𝗧𝗟/𝗘𝗟𝗧 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 End-to-end Python Data Engineer pipeline design and implementation: extraction, transformation, loading, orchestration, and monitoring. I work with Airflow, dbt, and Dagster for workflow management, BigQuery and PostgreSQL as primary data stores, and GCP and Azure as cloud infrastructure. Full logging, alerting (Slack, Telegram, email), and failure recovery included as standard. Teams save 30–80+ hours per month through ETL pipeline automation. 🕸 𝗣𝘆𝘁𝗵𝗼𝗻 𝗪𝗲𝗯 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 Large-scale Python Web Scraping development using Scrapy, Selenium, Playwright, and BeautifulSoup. My Web Scraping Data Extraction systems handle anti-detection, proxy rotation, user-agent rotation, CAPTCHA bypass, deduplication, and rate limiting — so data collection runs continuously without manual intervention. Proven at 1M+ rows/day. Suitable for lead generation scraping, ecommerce Data Extraction, market research, and competitive intelligence. 🧠 𝗚𝗖𝗣 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆 & 𝗖𝗹𝗼𝘂𝗱 𝗗𝗮𝘁𝗮 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Data lake and data warehouse architecture on GCP (BigQuery, Cloud Composer, Cloud Storage) and Azure. As a Python Data Engineer focused on performance, I apply query optimization, indexing, and partitioning to reduce query times by 50–70%. Also experienced with AWS (EC2, S3) for storage and pipeline deployment. 📊 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗕𝗜 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Looker, Apache Superset, and Excel dashboards connected directly to Big Data pipelines - so business teams get clean, decision-ready outputs without touching raw data or waiting for engineering. 🤖 𝗔𝗜/𝗠𝗟 & 𝗢𝗽𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Machine learning integration (scikit-learn, TensorFlow) and OpenAI API within Big Data pipelines for predictive analytics, intelligent anomaly detection, and AI-powered automation. 🧰 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Python · SQL · Pandas · PySpark · Scrapy · Playwright · Selenium · BeautifulSoup · Airflow · dbt · Dagster · BigQuery · PostgreSQL · MySQL · Firestore · GCP · Azure · AWS (EC2, S3) · REST API · Looker · Apache Superset · scikit-learn · TensorFlow · OpenAI API ✅ 𝗞𝗲𝘆 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: → 3 hours → 2 minutes: report generation via automated ETL pipeline → 99.95% uptime: Web Scraping system at 30K pages/day → 50–70% query time reduction through BigQuery optimization on GCP → 25% increase in dashboard engagement through interactive BI → Automated daily reports used by 50+ person internal teams via Python Web Scraping and Data Extraction pipelines 🔍 𝗕𝗲𝘀𝘁 𝗙𝗶𝘁 𝗙𝗼𝗿: → Startups building Big Data infrastructure from scratch → SaaS businesses automating ETL/ELT pipelines and reporting → Ecommerce teams using Python Web Scraping for pricing and competitor Data Extraction → Companies replacing manual workflows with scalable Web Scraping pipelines → Teams adding AI/ML capabilities to existing Big Data Engineer-designed infrastructure 📩 If you're looking for a technical partner who operates as both a reliable Data Engineer and a forward-thinking AI developer - someone who builds systems that run reliably, scale effortlessly, and enable better decisions - let's connect. 𝗟𝗲𝘁'𝘀 𝘁𝘂𝗿𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗮 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗮𝘀𝘀𝗲𝘁 - 𝗲𝘃𝗲𝗻 𝘄𝗵𝗶𝗹𝗲 𝘆𝗼𝘂 𝘀𝗹𝗲𝗲𝗽. 🚀