🌊 Banner 91/367
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+ scheduled runs · 18,200+ total CI events · 367 mutations · idempotent commits
| File | Version | Description |
|---|---|---|
| update_readme.py |  |
Banner engine + NLG + JSONL pipeline |
| build_next_badge.py |  |
HLS gradient renderer + countdown |
| Workflow | Schedule | Runs | Status |
|---|---|---|---|
| Auto Update README | Daily 12:15 UTC | 5,097 |  |
| Next Update Badge | Every 20min | 7,170 |  |
| CI/CD Pipeline | On push/PR | 5,665 |  |
| Smoke Tests | Daily | 244 |  |
| Cache GitHub Trophies | Every 6h | 8 |  |
| Generate Snake | Daily 00:30 UTC | 243 |  |
| Extra Badges | Daily 10:30 UTC | 3 |  |
.
├─ 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
│ ├─ trophies.svg # Cached GitHub Trophies SVG (via Cache GitHub Trophies workflow)
│ ├─ snake_variant.json # Active snake color variant (label/color, updated by snake.yml)
│ ├─ github_contributions.json # Total contributions this year (GraphQL, via badges_extra.yml)
│ ├─ github_commits.json # Commit count this year (GraphQL, via badges_extra.yml)
│ └─ github_issues.json # Issues opened this year (GraphQL, via badges_extra.yml)
└─ .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 · 🏆 trophies.svg · 🐍 snake_variant.json · 📊 github_contributions.json · 🔨 github_commits.json · 🐛 github_issues.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 | Demo |
|---|---|---|
| 🌤️ US Weather Pipeline | 10 cities · Comfort Index · Severity Score · 2×/day auto | 🤗 Live |
| 🛒 Olist Analytics | dbt · 54 tests · $13.2M · 96K orders | 🤗 Live |
| 🚗 Uber Driver Analytics | 3,448 trips · $70K gross · 98.9% rating | 🤗 Live |
| 🏢 HR BI Analytics | 30 employees · 5 depts · Sales $102K avg · Tableau | — |
| 📊 Business SQL Analytics | 2,314 cust · 5K transactions · $2.58M · 59.8% returning | — |
| 🦆 NYC 311 DuckDB | 22,504 records · Bronx 41.5% · DuckDB · MotherDuck | — |
| 🔄 ETL Pipeline | Faker → PostgreSQL · SQLFluff CI · Docker | — |
| 🤖 MLOps Project | R²=0.8326 · RMSE $46K · MLflow + W&B · 729 GridSearch | — |
| 🌍 Remote Job Tracker | 100 listings · 5% remote · Munich 36% · API→Tableau | — |
⚡ 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): 2026-05-26 13:57 UTC
- 🤖 Run: #5756 — open run
- 🧬 Commit: d50d9d3 — open commit
- ♻️ Updates (total): 369
- 🌀 Workflow: Auto Update README · Job: update-readme
- ✨ Event: schedule · 🧑💻 Actor: evgeniimatveev
- 🕒 Schedule: 24h_5m
- 🌈 Banner: 91/367
🗂️Recent updates (last 5)
| Time (UTC) | Run | SHA | Banner | Event/Actor | Insight |
|---|---|---|---|---|---|
| 2026-05-26 13:57:54 | 5756 | d50d9d3 |
91/367 (91.gif) | schedule/evgeniimatveev | 📡 ETL → FEATURES → IMPACT • RUN #5756 — Seed new schemas, grow reliable models 🌱 | Keep up the momentum! 🔥 Perfect time for CI/CD ma… |
| 2026-05-25 14:00:05 | 5755 | deeb031 |
90/367 (90.gif) | schedule/evgeniimatveev | 📡 PIPELINES, NOT FIRE-DRILLS • RUN #5755 — Rebuild With Lighter Dependencies 🌿 | Start Your Week Strong! 🚀 Ship A Thin Slice: API → … |
| 2026-05-24 13:24:21 | 5754 | 8b7b3b6 |
89/367 (89.gif) | schedule/evgeniimatveev | 📡 BUILD • MEASURE • LEARN • RUN #5754 — Pollinate features across teams 🐝 | Prep for an MLOps-filled week! ⏳ Profile the hotspots, c… |
| 2026-05-23 13:26:22 | 5753 | 06aef48 |
88/367 (88.gif) | schedule/evgeniimatveev | 📡 MLOPS DAILY • RUN #5753 — Refresh docs, replant ownership maps 🗺️ | Weekend automation vibes! 🎉 Profile queries, add indexes, save… |
| 2026-05-22 13:48:40 | 5752 | 380d311 |
87/367 (87.gif) | schedule/evgeniimatveev | 📡 BUILD • MEASURE • LEARN • RUN #5752 — Seed New Schemas, Grow Reliable Models 🌱 | Wrap It Up Like A Pro! ⚡ Make It Boring: Stable, … |
Night-mode palettes · Daily A–N theme rotation · 14 colors · Fully automated via GitHub Actions
| 📊 Data Analyst | 🔧 Data Engineer | 🤖 MLOps Engineer |
|---|---|---|
| SQL · Tableau · Power BI | PostgreSQL · DuckDB · dbt · Docker | MLflow · W&B · XGBoost · FastAPI |
| Dashboards → KPIs → Decisions | Raw Data → Pipelines → Production | Train → Track → Deploy → Monitor |
🤖 MLOPS Insight: 📡 ETL → FEATURES → IMPACT • RUN #5756 — Seed new schemas, grow reliable models 🌱 | Keep up the momentum! 🔥 Perfect time for CI/CD magic ⚡ 🧼
📈 Auto GitHub Insights (UTC · auto-refresh)
Daily contributions (last 30 days)
