Tech Lead · Senior Backend Engineer

I engineer the decision layer
between complexity and production.

Eleven years turning transport operations, distributed systems and backend complexity into software teams can trust, understand and evolve.

Explore the work↘ Email me

SOUSSE · TUNISIA11 YEARS / EXPERIENCEBACKEND · DATA · AI

NODE.JS◆RUST◆MICROSERVICES◆TRANSPORT TECH◆METABASE◆AI REVIEW◆SKILL ENGINEERING◆NODE.JS

Profile

Code is abundant.
Judgment is the multiplier.

A backend engineer who sees the whole operating system: domain, data, architecture, delivery and people.

I build scalable backend and microservice architectures, with strong practice across Node.js, Rust, AWS and distributed systems. My transport-domain work connects operational reality to dependable software—from EDI ingestion and tracking to delivery workflows.

As a Tech Lead, I turn ambiguity into explicit decisions, review what matters and help teams ship systems that remain understandable in production.

11years of experience

360°architecture to operations

H×AIhuman judgment, AI leverage

Operating systems

Four lenses.
One engineering practice.

Not isolated skills—a connected way to move from business signal to production outcome.

ACTIVE LENS / 01

Backend architecture

Service boundaries, microservices, APIs and distributed workflows designed for resilience, observability and change.

Node.jsRustMicroservicesGraphQL / REST

Operational intelligence

METABASE / SQL / KPI

From raw operations
to decisions people can act on.

I design and maintain Metabase dashboards, define operational KPIs, optimize SQL queries and build drill-down journeys that make transport activity, tours, delivery points and exceptions easier to follow.

Example transport monitoring dashboard

Visual example

MONITORING FILTERS

Delivery points—Drill by day / tour

Tours—Cross-filter enabled

Exceptions—Traceable to source

100500

Illustrative delivery-monitoring example — no real data displayed.

AI-augmented engineering

THE REVIEWER ADVANTAGE

Everyone can generate code.
Few can engineer confidence.

The advantage is no longer typing speed. It is the ability to frame the problem, supply the right context, design prompts and reusable skills, review architecture, test behavior and reject plausible-looking mistakes.

01?

Frame

Turn ambiguity into a testable brief.

02{ }

Context

Give agents the architecture, constraints and tools.

03✓

Review

Challenge logic, security, data and maintainability.

04↗

Ship

Verify in the real system and capture the learning.

Prompt architecture

Prompts as executable briefs: explicit goal, constraints, context and acceptance checks.

Reusable skills

Codifying repeatable engineering workflows instead of restarting from zero.

Agent orchestration

Matching models, tools and task boundaries to the work—not the hype.

Critical review

Architecture judgment and verification remain the production gate.

Experience

Experience with
operational weight.

Verified information from the supplied CV. Earlier career detail can be added when the remaining pages are available.

03 / 2023Present

BOX2HOME BY WARNING

Tech Lead

Technical leadership for a transport and logistics solution supporting delivery, assembly and installation of bulky goods, connecting operational workflows with scalable backend services.

Microservice architecture and backend scalability
EDI parser for XML, EDI, TXT and Excel client formats
Transport workflows, tracking and integration reliability
Technical direction, review and team enablement

Node.jsRustAWSMicroservicesEDI

Education

2014—2015

National Engineering Degree in Computer Science — Teleinformatics

ISITCOM HAMMAM SOUSSE

2009—2012

Applied Degree in Computer Networks

ISITCOM HAMMAM SOUSSE

Decision room

Tools change.
Decision quality compounds.

My value is not knowing a longer list of technologies. It is knowing what must be decided, what must be measured and what must never pass review unnoticed.

01 / BOUNDARIES

Where should the system be divided?

Separate by business responsibility, change pressure and failure mode—not fashion.

MICROSERVICES · APIs · EVENTS

02 / FAILURE

What must fail safely?

Design retries, idempotency, traceability and recovery before production teaches the lesson.

EDI · DISTRIBUTED FLOWS · OBSERVABILITY

03 / SIGNAL

Which signal deserves attention?

Turn operational questions into KPIs people can understand, explore and act on.

METABASE · SQL · TRANSPORT KPI

04 / JUDGMENT

Where must AI be challenged?

At every point where plausible output could become architecture, data loss or production risk.

PROMPTS · SKILLS · REVIEW

Contact

A difficult system is a good place to start.

Let’s engineer
the next move.

mehdi.bcheikh@gmail.com ↗

SOUSSE · TUNISIA