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: 06 August 2026 - To: 13 August 2026
Total Time: 4 hrs 9 mins
SQL 2 hrs 14 mins █████████████▒░░░░░░░░░░░ 53.17 %
Markdown 56 mins █████▓░░░░░░░░░░░░░░░░░░░ 22.50 %
PowerShell 46 mins ████▓░░░░░░░░░░░░░░░░░░░░ 18.32 %
Python 11 mins █░░░░░░░░░░░░░░░░░░░░░░░░ 04.64 %
- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [Your AI Agent Doesn't Need More Memory. It Needs Receipts.](https://dev.to/anasbuilds997/your-ai-agent-doesnt-need-more-memory-it-needs-receipts-1e3m) Sun Aug 16 2026 1:15 AM- [Qdrant Recall Inconsistency: It Took 300 Test Runs to Discover the Index Wasn't Refreshed](https://dev.to/_eb7f2a654e97a60ae9f96e/qdrant-recall-inconsistency-it-took-300-test-runs-to-discover-the-index-wasnt-refreshed-3agf) Sun Aug 16 2026 1:05 AM- [The most boring service in the stack](https://dev.to/kayashaolu/the-most-boring-service-in-the-stack-2kim) Sun Aug 16 2026 1:03 AM- [I Asked the Same Question to 7 Local LLMs — Speed and Intelligence Didn't Line Up: DGX Spark Benchmarks](https://dev.to/ebibibi/i-asked-the-same-question-to-7-local-llms-speed-and-intelligence-didnt-line-up-dgx-spark-4mg1) Sun Aug 16 2026 1:01 AM- [Software marketplaces have forgotten who they're supposed to serve](https://dev.to/lyrinox/software-marketplaces-have-forgotten-who-theyre-supposed-to-serve-450l) Sun Aug 16 2026 12:50 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!