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
|
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).
|
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.
|
|
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.
|
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.
|
From: 26 July 2026 - To: 02 August 2026
Total Time: 7 hrs 40 mins
Markdown 3 hrs 22 mins ███████████░░░░░░░░░░░░░░ 43.80 %
Python 1 hr 32 mins █████░░░░░░░░░░░░░░░░░░░░ 20.03 %
Text 1 hr 16 mins ████░░░░░░░░░░░░░░░░░░░░░ 16.52 %
SQL 48 mins ██▓░░░░░░░░░░░░░░░░░░░░░░ 10.54 %
Docker 30 mins █▓░░░░░░░░░░░░░░░░░░░░░░░ 06.62 %
JSON 8 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.79 %
PowerShell 2 mins ░░░░░░░░░░░░░░░░░░░░░░░░░ 00.44 %
Protocol Buffer 0 secs ░░░░░░░░░░░░░░░░░░░░░░░░░ 00.08 %
- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [I Audited My AI's To-Do List. A Quarter of It Was Already Done.](https://dev.to/nickmeinhold/i-audited-my-ais-to-do-list-a-quarter-of-it-was-already-done-1k01) Wed Aug 05 2026 2:35 AM- [Don't show users a raw Playwright error — translating exceptions into friendly messages](https://dev.to/susumun/dont-show-users-a-raw-playwright-error-translating-exceptions-into-friendly-messages-18d0) Wed Aug 05 2026 2:30 AM- [The Unhinged Reality Of Engineering Part One: It Wasn’t The Lock](https://dev.to/khalidelokiely/the-unhinged-reality-of-engineering-part-one-it-wasnt-the-lock-lhk) Wed Aug 05 2026 2:26 AM- [Prompt engineering couldn't fix this LLM bug. 20 lines of binary parsing did.](https://dev.to/socialfuel/prompt-engineering-couldnt-fix-this-llm-bug-20-lines-of-binary-parsing-did-3n37) Wed Aug 05 2026 2:23 AM- [Beyond RAG: Building an AI Coding Agent with Planning, Tool Execution, and ReAct Reasoning](https://dev.to/sri_d_6dfd4d31319a6389eaa/beyond-rag-building-an-ai-coding-agent-with-planning-tool-execution-and-react-reasoning-53ko) Wed Aug 05 2026 2:23 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!