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: 19 June 2026 - To: 26 June 2026
Total Time: 17 hrs
Batchfile 5 hrs 8 mins ██████▒░░░░░░░░░░░░░░░░░░ 25.73 %
Markdown 4 hrs 55 mins ██████▒░░░░░░░░░░░░░░░░░░ 24.68 %
PowerShell 3 hrs 5 mins ████░░░░░░░░░░░░░░░░░░░░░ 15.49 %
ASP.NET 1 hr 23 mins █▓░░░░░░░░░░░░░░░░░░░░░░░ 07.01 %
Python 1 hr 21 mins █▓░░░░░░░░░░░░░░░░░░░░░░░ 06.81 %
SQL 57 mins █▒░░░░░░░░░░░░░░░░░░░░░░░ 04.77 %
XML 3 mins ░░░░░░░░░░░░░░░░░░░░░░░░░ 00.30 %
- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [V.E.L.O.C.I.T.Y.-OS: The JIT Compiler Core – From AST to Native Closures (Part 4)](https://dev.to/unitbuilds_cc/velocity-os-the-jit-compiler-core-from-ast-to-native-closures-part-4-52f3) Sun Jun 28 2026 1:44 PM- [Before the Algorithm: Building the Input Layer for My Poker Analysis Tool](https://dev.to/ty215/before-the-algorithm-building-the-input-layer-for-my-poker-analysis-tool-4ape) Sun Jun 28 2026 1:41 PM- [V.E.L.O.C.I.T.Y.-OS: Ditching the Web Stack & The 30MB Standalone IDE (Part 3)](https://dev.to/unitbuilds_cc/velocity-os-ditching-the-web-stack-the-30mb-standalone-ide-part-3-3ia2) Sun Jun 28 2026 1:33 PM- [Just Watch](https://dev.to/codenameone/just-watch-13j9) Sun Jun 28 2026 1:31 PM- [n8n Airtable Node: Read, Create, Update, and Delete Records (Free JSON)](https://dev.to/pirateprentice/n8n-airtable-node-read-create-update-and-delete-records-free-json-5462) Sun Jun 28 2026 1:30 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!