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: 07 July 2026 - To: 14 July 2026
Total Time: 31 hrs 26 mins
Markdown 11 hrs 39 mins ████████▒░░░░░░░░░░░░░░░░ 33.72 %
PowerShell 9 hrs 3 mins ██████▓░░░░░░░░░░░░░░░░░░ 26.20 %
Python 4 hrs 17 mins ███░░░░░░░░░░░░░░░░░░░░░░ 12.43 %
SQL 4 hrs 3 mins ███░░░░░░░░░░░░░░░░░░░░░░ 11.75 %
JSON 43 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 02.10 %
CSV 39 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.90 %
Text 17 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 00.85 %
- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [Keeping Match Confidence on the Graph Edge: Why Throwing Away Splink Scores Hurts Graph RAG](https://dev.to/hannune/keeping-match-confidence-on-the-graph-edge-why-throwing-away-splink-scores-hurts-graph-rag-140n) Fri Jul 17 2026 2:40 AM- [Why I Built Local-First Agent Memory](https://dev.to/labyrinthanalytics/why-i-built-local-first-agent-memory-237p) Fri Jul 17 2026 2:17 AM- [LangGraph vs LangChain in 2026: When Each Wins](https://dev.to/labyrinthanalytics/langgraph-vs-langchain-in-2026-when-each-wins-52nc) Fri Jul 17 2026 2:14 AM- [My AI QA agent said "all features working." The canvas was blank. Here's what it was actually seeing.](https://dev.to/mxhlix/my-ai-qa-agent-said-all-features-working-the-canvas-was-blank-heres-what-it-was-actually-1pl5) Fri Jul 17 2026 2:12 AM- [How I Built an AI Interview Preparation Platform](https://dev.to/zakkasmi/how-i-built-an-ai-interview-preparation-platform-14ob) Fri Jul 17 2026 2:11 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!