?
Understand the need
Users, goals, constraints, risks and success criteria before choosing tools.
DATA & AI TECH LEAD FRANCE
Complex data.
Useful products.
Omar Laraqui. I am a Data & AI Tech Lead, building reliable data platforms and practical products that connect engineering with business needs.
My direction: grow toward Forward Deployed Engineering through real projects, system integrations and measurable evidence.
8+years in data engineering
Azure/DBXcloud and lakehouse foundation
01→04discover, design, validate, deliver
ENGINEERING NOTES / 001FROM PROBLEM TO SOLUTION
01 / DISCOVERYReal user needs
02 / ENGINEERINGReliable integration
03 / DELIVERYUseful delivery
PROBLEMPRODUCT
SCROLL TO EXPLOREEVIDENCE BEFORE HYPE ↘
01 / SELECTED PROJECTS
Proof of work.
not just code
Six projects across data engineering, AI and integrations. Product source repositories remain private; architectural decisions are discussed publicly.
06 / 06 PROJECTS
Product engineering / Applied AI
DossierPro
From fragmented candidate information to structured, AI-assisted recruitment workflows.
CASE STUDY / PRIVATE CODEVIEW PROJECT
Data platform / AI analytics
Retail Churn Copilot
A conversational analytics application on top of a Databricks-native data platform.
CASE STUDY / PRIVATE CODEVIEW PROJECT
Integrations / Full-stack systems
Mission Radar
A private research and workflow cockpit connecting sources, enrichment and decisions.
CASE STUDY / PRIVATE CODEVIEW PROJECT
Field engineering / Technical methodology
SA-OS
A rigorous, evidence-led operating system for customer discovery and solution architecture.
CASE STUDY / PRIVATE CODEVIEW PROJECT
Information systems / Research tooling
BLOOM Terminal
A focused intelligence workspace for cloud, data and technology research.
CASE STUDY / PRIVATE CODEVIEW PROJECT
Computer vision / Consumer product
PokéValue
A mobile-first vision and market-data application, from image to useful portfolio action.
CASE STUDY / PRIVATE CODEVIEW PROJECT
These case studies are grounded in repository documentation; they do not independently verify production adoption or measured business impact.
02 / MY APPROACH
From messy brief
to reliable system.
My process combines business understanding, Data architecture, hands-on development and pragmatic validation — the foundation I am building on for FDE.
◇
Choose the right architecture
Define integrations, data and AI components with clear trade-offs.
≋
Test the assumptions
Reproducible tests, realistic data, evaluations and observability.
↗
Deliver and improve
Useful, secure operational systems with a feedback loop.
03 / PROFESSIONAL EXPERIENCE
Real-world experience.
as a foundation
Data engineering engagements across banking, insurance, retail and energy — from implementation through architecture and technical leadership.
ENGIE
Data Tech Lead
Groupe Alain Afflelou
Data Architect & Tech Lead
iQera
Senior Cloud Data Engineer
04 / BUILDING TOWARD FDE
The next chapter
is in progress.
To grow into an FDE role, I plan to publish reproducible labs with working integrations, evaluations, code and limitations. These are planned, not completed deliveries.
Explore the roadmap05 / OPEN-SOURCE FOUNDATIONS
Built over time.
Earlier public experiments that show the breadth of my engineering journey.
COVID-19 Data API
Public data ingestion and Python API — historical archived project.
World Inequality Visuals
Interactive D3.js analytics and data storytelling.
Hex Game AI
Python algorithms, AI game agent and graphical client / server.
BUILDING USEFUL SYSTEMS