🌊 Banner 86/367
Turning raw data into dashboards, pipelines into insights, and complexity into clarity
💡 I build end-to-end data pipelines and MLOps systems — from raw ingestion to production-ready dashboards — with a focus on Docker, CI/CD automation, DuckDB, and data storytelling that drives real business decisions.
🛠️ Core Stack
SQL • Python • PostgreSQL • DuckDB • Docker • GitHub Actions • MLflow • Tableau • Power BI• Excel
📊 Focus Data Engineering • MLOps Automation • Business Intelligence • ETL/ELT Pipelines • Analytics Engineering
⚡ How it works (architecture deep-dive 🔬 for engineers)
This profile is a self-updating MLOps demo — a living portfolio showcasing production-grade automation.
- 🤖 Banner rotation: 367 GIFs · natural sorting · cache-busted CDN URLs
- 🧩 Dynamic insights: Context-aware NLG (time/season/DOW algorithms)
- ⏱️ Next Update badge: Shields.io endpoint · HLS gradient · sub-minute precision
- 📡 Observability: JSONL telemetry · heartbeat pings · state persistence
- ⚙️ Zero-touch ops: 5,700+ runs · 350+ mutations · idempotent commits
| File | Version | Description |
|---|---|---|
| update_readme.py |  |
Banner engine + NLG + JSONL pipeline |
| build_next_badge.py |  |
HLS gradient renderer + countdown |
| Workflow | Schedule | Status |
|---|---|---|
| Auto Update README | Daily 12:15 UTC |  |
| Next Update Badge | Every 20min |  |
| CI/CD Pipeline | On push/PR |  |
.
├─ update_log.jsonl # CI run timeline (1 JSON per run: ts_utc, run_id, run_number, sha, banner_*, insight_*)
├─ update_log.txt # Grep-friendly mirror of update_log.jsonl (ts UTC, run=…, sha=…; rolling tail)
├─ badges/
│ ├─ next_update.json # Live Shields.io badge state (label, message like '~14h 35m', color bucket)
│ ├─ next_update_log.jsonl # Badge countdown snapshots (ts, next_utc, minutes_left, message, color, jitter params)
│ ├─ next_update_log.txt # Human-readable badge ETA tail ([ts] color=… msg='…' next_utc=… mins_left=…)
│ ├─ github_followers.json # Endpoint payload for the Followers badge (schemaVersion/label/message/color)
│ ├─ github_stars.json # Endpoint payload for the Stars badge
│ └─ total_updates.json # Endpoint payload for the Updates badge
└─ .ci/
├─ heartbeat.log # GitHub Actions heartbeat ledger (Updated on / Triggered by / Commit SHA / Run ID / Run number)
└─ update_count.txt # Monotonic mutation counter (powers the «N mutations shipped» tagline)
📋 Browse logs: 📊 update_log.jsonl · 📝 update_log.txt · 💓 heartbeat.log · 🔢 update_count.txt ⏱️ next_update.json · 📡 next_update_log.jsonl · 📋 next_update_log.txt 👥 github_followers.json · ⭐ github_stars.json · 📈 total_updates.json
Focus
- 📊 Data Analytics & Business Intelligence
- 🧠 Advanced SQL, Data Modeling & Analytical Thinking
- ⚙️ Analytics Engineering · ETL/ELT workflows · Pipeline automation
- ☁️ Cloud Analytics — Azure Databricks, Data Factory, Synapse Analytics
- 🐍 Python & R for data science workflows
🧭 2+ years of hands-on learning focused on real-world projects, production dashboards, and automated data pipelines
- 🎓 SuperDataScience — Data Analytics, ML & Automation
- 📘 Udemy — SQL, Tableau, Power BI & Data Projects
- ☁️ CloudWolf — AWS & Azure fundamentals for data workflows
- Build dashboards that answer real business questions (Tableau, Power BI)
- Write advanced SQL — CTEs, window functions, optimization, not just
SELECT * - Design and automate ETL/ELT pipelines end-to-end (Python, PostgreSQL, DuckDB)
- Model data for analytics — star schema, dimensional modeling, data contracts
- Work with cloud analytics stacks (Azure Databricks, Data Factory, Synapse)
- Turn raw data into decisions — fast, reproducible, and production-grade
| Project | Highlights |
|---|---|
| 🚗 Uber Driver Analytics | Supabase · Streamlit ·Docker |
| 🏢 HR BI Analytics | PostgreSQL · Tableau · Python |
| 📊 Business SQL Analytics | $2.58M rev · 2,314cust. |
| 🦆 NYC 311 DuckDB | DuckDB · MotherDuck |
| 🔄 ETL Pipeline | Python · Docker · 2,500+ CI |
| 🤖 MLOps Project | XGBoost · MLflow · R²=0.8326 |
| 🌍 Remote Job Tracker | REST API · Tableau · 100 jobs |
⚡ AI-Powered Engineering Workflow
| Assistant | Role | Usage |
|---|---|---|
| 🧠 Claude Sonnet 4.6 | Primary AI Partner — architecture · code · analytics · docs · review | Primary |
💡 How Claude fits into my workflow
Claude Sonnet 4.6 is my primary AI engineering partner across all stages of the data & MLOps lifecycle:
- 🏗️ Architecture → pipeline design, schema decisions, project structure
- 🐍 Code → Python scripts, SQL queries, Docker configs, GitHub Actions workflows
- 📊 Analytics → data modeling, query optimization, business logic translation
- 📝 Documentation → READMEs, project descriptions, technical write-ups
- 🔍 Review → debugging, code quality, edge case analysis
Precision-first · Context-aware · Production-grade output.
🤖 Automation Logs
🪄 Run Meta (click to expand)
- 📆 Updated (UTC): 2026-05-21 14:02 UTC
- 🤖 Run: #5751 — open run
- 🧬 Commit: b9f48f3 — open commit
- ♻️ Updates (total): 364
- 🌀 Workflow: Auto Update README · Job: update-readme
- ✨ Event: schedule · 🧑💻 Actor: evgeniimatveev
- 🕒 Schedule: 24h_5m
- 🌈 Banner: 86/367
🗂️Recent updates (last 5)
| Time (UTC) | Run | SHA | Banner | Event/Actor | Insight |
|---|---|---|---|---|---|
| 2026-05-21 14:02:18 | 5751 | b9f48f3 |
86/367 (86.gif) | schedule/evgeniimatveev | 📡 LOW TOIL, HIGH LEVERAGE • RUN #5751 — Seed new schemas, grow reliable models 🌱 | Test, iterate, deploy! 🚀 Review metrics, cut toil… |
| 2026-05-20 13:52:16 | 5750 | 76b4d6b |
85/367 (85.gif) | schedule/evgeniimatveev | 📡 MLOPS DAILY • RUN #5750 — Spring into automation! 🪴 | Halfway there — keep automating! 🛠️ Optimize, deploy, repeat! 🔄 ❄️ |
| 2026-05-19 13:56:01 | 5749 | 8120708 |
84/367 (84.gif) | schedule/evgeniimatveev | 📡 REPRODUCIBILITY FIRST • RUN #5749 — Refactor And Bloom 🌼 | Keep Up The Momentum! 🔥 Keep Pushing Your Mlops Pipeline Forward! 🔧 🧱 |
| 2026-05-18 14:05:55 | 5748 | 13d933c |
83/367 (83.gif) | schedule/evgeniimatveev | 📡 ETL → FEATURES → IMPACT • RUN #5748 — SPRING-CLEAN ORPHAN TABLES AND DAGS 🧽 | START YOUR WEEK STRONG! 🚀 OPTIMIZE, DEPLOY, REPEAT! 🔄 📈 |
| 2026-05-17 13:23:51 | 5747 | d917508 |
82/367 (82.gif) | schedule/evgeniimatveev | 📡 TEST • OBSERVE • DEPLOY • RUN #5747 — Plant Ideas, Water Pipelines 🌱 | Prep For An Mlops-filled Week! ⏳ Measure → Iterate → Ship 🚀 🌊 |
Night-mode palettes · Daily A–G theme rotation · Fully automated via GitHub Actions
| 🧩 Workflow | ⚙️ Automation | 📊 Insights |
|---|---|---|
| Raw Data → Clean → Analyze | ETL → CI/CD → Automated Pipelines | Dashboards → KPIs → Decisions |
| Build → Visualize → Scale | Python · R · GitHub Actions · Docker | Measure → Learn → Improve |
🤖 MLOPS Insight: 📡 LOW TOIL, HIGH LEVERAGE • RUN #5751 — Seed new schemas, grow reliable models 🌱 | Test, iterate, deploy! 🚀 Review metrics, cut toil, add value 📉→📈 🧾
📈 Auto GitHub Insights (UTC · auto-refresh)
Daily contributions (last 30 days)
