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: 10 June 2026 - To: 17 June 2026
Total Time: 22 hrs 58 mins
Markdown 8 hrs 33 mins ███████▒░░░░░░░░░░░░░░░░░ 29.93 %
Python 5 hrs 28 mins ████▓░░░░░░░░░░░░░░░░░░░░ 19.14 %
PowerShell 3 hrs 3 mins ██▓░░░░░░░░░░░░░░░░░░░░░░ 10.70 %
SQL 2 hrs 33 mins ██▒░░░░░░░░░░░░░░░░░░░░░░ 08.97 %
JSON 1 hr 56 mins █▓░░░░░░░░░░░░░░░░░░░░░░░ 06.78 %
JavaScript 18 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.09 %
YAML 17 mins ▒░░░░░░░░░░░░░░░░░░░░░░░░ 01.03 %
- 🔒 Closed issue #1 in mojiTMJ/mojiTMJ
- [I built an open-source market maker for prediction markets (Polymarket/CLOB) — here's how it works](https://dev.to/harrieronchain/i-built-an-open-source-market-maker-for-prediction-markets-polymarketclob-heres-how-it-works-5aam) Fri Jun 19 2026 3:16 PM- [How I Slashed AI API Costs 60% as a Cloud Architect](https://dev.to/swift-logic-io218/how-i-slashed-ai-api-costs-60-as-a-cloud-architect-3n6) Fri Jun 19 2026 3:11 PM- [GBase 8a Query Optimization in Practice: EXPLAIN, Materialized Views, CTE, and Common Tuning Techniques](https://dev.to/michaelfv/gbase-8a-query-optimization-in-practice-explain-materialized-views-cte-and-common-tuning-1d4j) Fri Jun 19 2026 3:10 PM- [Agentic Coding in Mid-2026: What Changed and How I Actually Use It](https://dev.to/pavelespitia/agentic-coding-in-mid-2026-what-changed-and-how-i-actually-use-it-2kl4) Fri Jun 19 2026 3:09 PM- [We Open-Sourced a 500-Line Python Module That Solves Agent Crash Recovery](https://dev.to/hhhfs9s7y9code/we-open-sourced-a-500-line-python-module-that-solves-agent-crash-recovery-22m8) Fri Jun 19 2026 3:07 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!