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"],
};|
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: 29 June 2026 - To: 06 July 2026
Total Time: 19 hrs 4 mins
Markdown 9 hrs 50 mins ████████████▒░░░░░░░░░░░░ 48.87 %
PowerShell 5 hrs 53 mins ███████▒░░░░░░░░░░░░░░░░░ 29.30 %
SQL 1 hr 42 mins ██░░░░░░░░░░░░░░░░░░░░░░░ 08.51 %
ASP.NET 40 mins █░░░░░░░░░░░░░░░░░░░░░░░░ 03.37 %
JSON 20 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.70 %
Python 10 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 00.91 %
CSV 6 mins ░░░░░░░░░░░░░░░░░░░░░░░░░ 00.58 %- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [Why Your ChatGPT Answers Feel Generic (It's Not the Model's Fault)](https://dev.to/kaelctu3/why-your-chatgpt-answers-feel-generic-its-not-the-models-fault-3mcf) Thu Jul 09 2026 3:25 AM- [Pytest Pt1 - Fundamentals for Data Engineers](https://dev.to/felipe_de_godoy/pytest-fundamentals-for-data-engineers-43o9) Thu Jul 09 2026 3:18 AM- [2026 Technical Comparison: Stock & Forex Historical Market Data APIs – Capabilities & Integration Workflows](https://dev.to/kels180/2026-technical-comparison-stock-forex-historical-market-data-apis-capabilities-integration-39i7) Thu Jul 09 2026 3:18 AM- [8 Free Food & Nutrition APIs (No Key, Tested 2026)](https://dev.to/0012303/8-free-food-nutrition-apis-no-key-tested-2026-3doh) Thu Jul 09 2026 3:18 AM- [Compilable and Executable Pseudocode (spec) Solves AI Coding Hallucinations](https://dev.to/esproc_spl/compilable-and-executable-pseudocode-spec-solves-ai-coding-hallucinations-5ghk) Thu Jul 09 2026 3:08 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 ☕

