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: 28 June 2026 - To: 05 July 2026
Total Time: 22 hrs 57 mins
Markdown 13 hrs 37 mins ██████████████▒░░░░░░░░░░ 56.73 %
PowerShell 5 hrs 55 mins ██████░░░░░░░░░░░░░░░░░░░ 24.64 %
SQL 1 hr 42 mins █▓░░░░░░░░░░░░░░░░░░░░░░░ 07.14 %
ASP.NET 40 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 02.83 %
JSON 20 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.42 %
Python 14 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.01 %
CSV 6 mins ░░░░░░░░░░░░░░░░░░░░░░░░░ 00.48 %
- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [Stop Fixing Your AI Writing Prompt. Make These 5 Decisions First](https://dev.to/nomurasan/stop-fixing-your-ai-writing-prompt-make-these-5-decisions-first-51gk) Tue Jul 07 2026 3:22 AM- [Linux Package Management Explained Simply (apt, dnf, yum & rpm)](https://dev.to/sreekanth_kuruba_91721e5d/linux-package-management-explained-simply-apt-dnf-yum-rpm-11gn) Tue Jul 07 2026 3:13 AM- [DeepSeek vs Qwen vs Kimi vs GLM: Which AI API Actually Wins in 2025?](https://dev.to/gentlenode/deepseek-vs-qwen-vs-kimi-vs-glm-which-ai-api-actually-wins-in-2025-8bn) Tue Jul 07 2026 3:02 AM- [Residential Proxies for Developers: Picking the Right IP Strategy (2026 Comparison)](https://dev.to/danielk_automat/residential-proxies-for-developers-picking-the-right-ip-strategy-2026-comparison-3l0m) Tue Jul 07 2026 2:54 AM- [INTO Coding...](https://dev.to/mark_tony_848153460063d29/into-coding-1ic0) Tue Jul 07 2026 2:53 AM
- 🏗️ 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!