I'm Jalalledin "Moji" Taavoni — a Data Engineer (Azure data platform · SQL Server · BI) who also takes AI to production, based in Milano 🇮🇹.
I build the unglamorous machinery that makes data trustworthy: metadata-driven ETL, star-schema datamarts, incremental loads that survive 2 a.m., and the CI/CD + governance around them. Then I bring AI to production the same way — from notebook demo to a system that runs reliably, observably, and at the right cost.
const moji = {
role: ["Data Engineer", "DataOps / Data Platform", "AI Integration (production)"],
stack: ["SQL Server", "Azure Data Factory", "Synapse", "Fabric", "SSIS", "SSAS",
"Power BI", "Databricks", "dbt", "Neo4j", "Python", "Azure", "LangChain"],
philosophy: "Thoughtful before fancy.",
education: "Computer Science + Digital Humanities · Università di Pisa",
currently: "Metadata-driven datamarts on Azure — and taking AI to production",
open_to: "Freelance & contract · IT and Remote EU",
reach: ["mojitmj.github.io", "linkedin.com/in/mojitmj", "t.me/mojitmj"],
};☁️ Cloud · Platform · DevOps
💾 Data Engineering · DataOps
🧠 AI · ML · LLMs (the growth edge)
💻 Languages · Tools
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PowerShell tool that x-rays a SQL Server / Azure SQL instance in one command — full DDL, DMVs, backup history, security audit, design-quality checks, per-table data samples. Cross-platform schedulers (Task Scheduler · SQL Agent · SSIS · cron · systemd).
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Metadata-driven Azure Data Factory ingestion template — managed-identity auth, multi-env CI/CD (dev/staging/prod), and PR validation (JSON schema + hardcoded-secret scanning). Drop-in for any ADF estate.
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Digital-humanities side project: 175 years of Italian academies as a property graph in Neo4j, visualized in the browser with popoto.js. Where data engineering meets the archive.
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Live portfolio: dual-positioning landing page (AI / DataOps / DE / BI / DA), animated streaming-source boot, EN/IT toggle with Italian-flag theme, live chat overlay, full visitor metadata pipeline.
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From: 22 August 2026 - To: 29 August 2026
Total Time: 11 hrs 3 mins
Markdown 7 hrs 40 mins █████████████░░░░░░░░░░░░ 51.44 %
Python 2 hrs 5 mins ███▓░░░░░░░░░░░░░░░░░░░░░ 14.02 %
PowerShell 37 mins █░░░░░░░░░░░░░░░░░░░░░░░░ 04.23 %
TOML 20 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 02.27 %
YAML 11 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.30 %
Makefile 4 mins ░░░░░░░░░░░░░░░░░░░░░░░░░ 00.54 %
Terraform 1 min ░░░░░░░░░░░░░░░░░░░░░░░░░ 00.21 %
- 🎉 Merged PR #5 in mojiTMJ/mojiTMJ
- [Backend Frameworks Under the Hood: What They Actually Do For You](https://dev.to/oketch/backend-frameworks-under-the-hood-what-they-actually-do-for-you-2a61) Mon Aug 31 2026 6:49 PM- [ChatGPT como motor de busca: como adequar sua startup ao DSA](https://dev.to/leojulieta/chatgpt-como-motor-de-busca-como-adequar-sua-startup-ao-dsa-4n70) Mon Aug 31 2026 6:45 PM- [Semrush Study Finds ChatGPT and Google AI Mode Create Different Brand Visibility Landscapes](https://dev.to/alifar/semrush-study-finds-chatgpt-and-google-ai-mode-create-different-brand-visibility-landscapes-3bn9) Mon Aug 31 2026 6:45 PM- [React Lazy Loading Images: A Simple Guide to Better Performance](https://dev.to/jaimin_patel/react-lazy-loading-images-a-simple-guide-to-better-performance-48hh) Mon Aug 31 2026 6:40 PM- [How To Develop Logic](https://dev.to/abimanyu_p_9e75124634d2a4/how-to-think-logic-65g) Mon Aug 31 2026 6:39 PM
- 🏗️ Data platform / DataOps — metadata-driven ETL, star-schema datamarts, lakehouse on ADF + Databricks, CI/CD, governance, FinOps
- 🔧 SQL Server modernization — legacy → Azure SQL / MI / Fabric with replayable migrations
- 📊 BI / Power BI rescues — slow reports, wrong numbers, ungoverned sprawl
- 🤖 Production AI — taking LLM / RAG / agent prototypes to systems that survive Tuesday morning
- 🛡️ AI evaluation & guardrails — golden sets, drift detection, regression gates, jailbreak hardening
- ⚡ Edge AI — Azure AI Foundry Local · ONNX · on-device LLMs for latency- or privacy-bound workloads
shipping: metadata-driven datamarts & ADF pipelines on Azure for IT/EU clients
building: sqlsnapshot v2 — Azure SQL DB + Fabric warehouse coverage
exploring: production AI on Azure + on-device LLMs (Phi-3, Llama-3) via Foundry Local
reading: "Designing Data-Intensive Applications" (annual re-read)
sipping: a long espresso ☕
"Pipelines should be like good plumbing — invisible, reliable, and someone else's problem when they break. I'm the someone else."
🟢🤍🔴 Fatto con espresso ☕ & 🍕 a Milano · Ciao mondo!