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: 09 June 2026 - To: 16 June 2026
Total Time: 27 hrs 54 mins
Markdown 12 hrs ████████▓░░░░░░░░░░░░░░░░ 35.13 %
Python 5 hrs 28 mins ████░░░░░░░░░░░░░░░░░░░░░ 16.00 %
PowerShell 3 hrs 30 mins ██▓░░░░░░░░░░░░░░░░░░░░░░ 10.27 %
SQL 3 hrs 29 mins ██▓░░░░░░░░░░░░░░░░░░░░░░ 10.20 %
JSON 1 hr 56 mins █▒░░░░░░░░░░░░░░░░░░░░░░░ 05.67 %
JavaScript 18 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 00.91 %
INI 17 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 00.87 %- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [How to Integrate Apache Kafka with Spring Boot: A Production-Ready Guide](https://dev.to/prashantsasalatti/how-to-integrate-apache-kafka-with-spring-boot-a-production-ready-guide-e6d) Thu Jun 18 2026 3:21 PM- [I built Proofline because AI agents are getting too good at sounding finished](https://dev.to/aisflows/i-built-proofline-because-ai-agents-are-getting-too-good-at-sounding-finished-2gm7) Thu Jun 18 2026 3:20 PM- [Gas Optimization That Doesn't Break Security: Storage, Calldata, and the Traps](https://dev.to/pavelespitia/gas-optimization-that-doesnt-break-security-storage-calldata-and-the-traps-1ba3) Thu Jun 18 2026 3:15 PM- [The Road Toward Mainnet: A Security-First Approach to XRPL Lending Protocol](https://dev.to/ripplexdev/the-road-toward-mainnet-a-security-first-approach-to-xrpl-lending-protocol-3bn6) Thu Jun 18 2026 3:15 PM- [Code Review Gone Wrong](https://dev.to/lavkeshdwivedi/code-review-gone-wrong-1g5o) Thu Jun 18 2026 3:09 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!