Infrastructure as Code
Terraform · CloudFormation · encrypted remote state · multi-AZ VPC · isolated data tiers · least-privilege IAM
→ An environment you can destroy and rebuild with confidence
Remote Worldwide · Every Time Zone · Available Now
John
Nessime — AWS DevOps Engineer, remote worldwide
AWS DevOps Engineer / Cloud & Data Infrastructure
I build the path from commit to production and make it boring — infrastructure in Terraform, releases through CI/CD, systems watched by Grafana, and data pipelines that keep reporting honest. Then I hand you the code, the dashboards and the runbooks so your team owns it.
AWS Certified
SOLUTIONS ARCHITECT — VERIFY
Live Monitoring Demo
REAL GRAFANA DASHBOARD
8+ Years
PRODUCTION CLOUD SYSTEMS
EN · FR · AR
WORKING LANGUAGES
// About
The engineer between
The engineer between
your code and your customers
I'm an AWS DevOps Engineer built on two deep specialisations: AWS solutions architecture and data engineering. That combination is the point. Most teams can get code deployed, or get a dashboard built — the hard part is making the whole path reliable: commit → pipeline → infrastructure → data → decision.
Most businesses don't have an infrastructure problem. They have a repeatability problem. Servers configured by hand two years ago that nobody dares touch. Deploys that need a specific person online. Reports rebuilt manually every Monday. Outages discovered by customers. Every one of those is a system that was never finished — and finishing them is the work I do.
How an engagement runs
- Understand the objective, the current architecture, and the constraints you actually have — budget, team size, compliance.
- Propose a practical, secure design, with the trade-offs written down rather than assumed.
- Build it as code: Terraform, pipelines, dashboards, and alerting — reviewed as we go.
- Test, document, and hand over. You get the repository, the runbooks, and admin access. No lock-in to me.
You get clear communication, organised documentation, and systems designed for real business use — not technical demonstrations. Send me your goal, your current stack, or the architecture problem that's been sitting in the backlog, and I'll tell you the most practical next step.
🇬🇧 English — Full Professional
🇫🇷 French — Full Professional
🇪🇬 Arabic — Native
// Expertise
What I actually do
DevOps first, then the two specialisations that make it useful — AWS architecture and data engineering. Every line below is something I've shipped into production, not a technology I've read about.
CI/CD & Release Automation
GitHub Actions · automated testing · container builds · gated deploys · fail-fast pipelines · fast rollback
→ Shipping becomes routine instead of an event
Observability & Monitoring
Grafana · Prometheus · Loki · Promtail · Alertmanager · CloudWatch · centralised logs · tested alert routing
→ You learn about incidents from a dashboard, not a customer
Cloud Security & Hardening
IAM policy design · ModSecurity WAF · CSF firewall · VPS hardening · CIDR blocking · TLS & certificate automation
→ A smaller blast radius when something does go wrong
AWS Solutions Architecture
EC2 · S3 · Lambda · API Gateway · DynamoDB · RDS · CloudFront · VPC · Well-Architected reviews
→ Architecture decisions you can defend in a review
Data Engineering & ETL
S3 data lakes · AWS Glue · Athena · Redshift · BigQuery · schema drift handling · idempotent reruns
→ Loads that survive a rerun, and an answer when two reports disagree
AI & Document Automation
Amazon Textract · Python OCR pipelines · S3 + Lambda · confidence thresholds with human review · structured export
→ Hours of manual data entry become a file drop
BI & Reporting Delivery
Power BI · DAX · Power Query · star-schema modelling · Tableau · Looker Studio · automated refresh
→ An executive KPI view that maintains itself
// Selected Work
All repositories on GitHub →
Systems running in production
Live platforms, open-source infrastructure, and productised services — each one links to something you can actually inspect: a running dashboard, a public repository, or a written case study.
Live Demo
Hosted Monitoring Platform
A managed Grafana stack I host for the client — API, website, cloud and server metrics with alerting and secure credential handover. The demo below is a real dashboard, not a screenshot.
Service
Self-Hosted Observability Platform
The full Grafana + Prometheus + Loki stack installed on your own servers via Docker Compose, with centralised log search and tested alert routing. Full admin access handed over — no SaaS or per-host fees.
Open Source
terraform-aws-baseline
A secure-by-default AWS account baseline in Terraform: encrypted remote state, a multi-AZ VPC with an isolated database tier, and least-privilege IAM. Start compliant instead of retrofitting security later.
Service
AI Invoice & Receipt OCR Pipeline
Event-driven document extraction on Textract and Lambda. Invoices, receipts and scans become clean CSV, Excel or database records — with low-confidence fields escalated to human review instead of silently corrupting your data.
Live Site
mJm Travel — Travel Platform
A large affiliate travel platform: Viator API integration, custom PHP and WordPress workflows, MySQL ingestion, SEO infrastructure and automated content routing — deployed and monitored on cloud infrastructure.
Open Source
travel-etl-pipeline
A serverless ETL pipeline for NYC travel data, ending in a Tableau reporting layer. Ingest, transform, warehouse, visualise — the full data engineering path in one repository.
Live App
Real Estate Price Predictor
An end-to-end machine learning application: feature engineering, regression models, and a deployed Streamlit interface for interactive property price estimation. Training through deployment, not just a notebook.
Service
AWS Architecture Review
A structured Well-Architected review of an existing account — security posture, cost leakage, reliability gaps and scaling limits — delivered as a prioritised remediation plan rather than a 60-page PDF nobody reads.
Service
Serverless API Backend
Scalable backends on Lambda, DynamoDB, API Gateway, S3 and IAM with event-driven workflows — defined in infrastructure as code, instrumented from day one, and documented for handover.
Open Source
flightops-lakehouse
A cost-safe AWS lakehouse over live flight telemetry. The same dbt models run on DuckDB locally and Athena in the cloud, and CI proves the whole pipeline — 215 tests, 97% coverage — with zero AWS credentials.
// From the Blog
Read all articles →
Cloud, DevOps & Data — built, tested, explained
I write up the problems I actually hit: the Terraform baseline, the pipeline that fails fast, the log label that wrecked a Loki install. Real debugging, with the trade-offs left in.
SageMaker Lakehouse vs Glue + Athena: Is Migration Worth It?
SageMaker Lakehouse is built on the Glue Data Catalog and Lake Formation, so a migration barely moves data. It moves enforcement…
Read article →
Your CRM Is Not a Database: Where GoHighLevel Should Stop
A CRM never pushes back when you add one more field, which is exactly why the schema drifts until someone asks a question that…
Read article →
Building a Hotel Data Lake on AWS That Agrees With the Night Audit
Hotel source data is mutable in the past, so an append-only pipeline drifts away from the PMS without anyone noticing until month…
Read article →
The Real Cost of CloudWatch: Logs, Metrics, Retention and Cardinality
CloudWatch rarely fails loudly, it accumulates. This is a breakdown of the four independent meters behind the bill: ingestion…
Read article →
// Certifications
Verified credentials
Every credential below links to the issuer's own record. AWS issues its certification badges through Credly, so the badge page is the official proof — issue date, expiry and assessed skills included.
Amazon Web Services
- AWS Solutions Architect – AssociateVerify
- AWS Data Visualization — DemonstratedVerify
- AWS Data Streaming — DemonstratedVerify
- AWS Data Lakehouse — DemonstratedVerify
- AWS Incident Response — DemonstratedVerify
- AWS Serverless — DemonstratedVerify
- AWS Cloud PractitionerVerify
- DevOps Engineer – ProfessionalIn Progress
- Data Engineer – AssociateIn Progress
- Building Data Lakes on AWS
- Migrating to the AWS Cloud
Google — Data & AI
- Google Data Analytics ProfessionalVerify
- Google AI Professional CertificateVerify
- Data Analytics Capstone: Case Study
- Process Data: From Dirty to Clean
- Prepare Data for Exploration
- Analyze Data to Answer Questions
- Share Data Through Visualization
- Data Analysis with R Programming
- AI for Data Analysis
- AI for App Building
Microsoft Power BI
- Power BI Data Analyst — ProfessionalCertified
- Extract, Transform and Load Data
- Data Modeling in Power BI
- Data Analysis & Visualization
- Creative Designing in Power BI
- Preparing Data with Microsoft Excel
- Harnessing the Power of Data
- Deploy & Maintain Assets — Capstone
- PL-300 Exam Preparation & Practice
// Questions
Before you get in touch
The things clients and hiring managers ask me first.
What does an AWS DevOps Engineer actually deliver?
A reproducible path from commit to production. In practice: your infrastructure defined in Terraform, a CI/CD pipeline that tests and deploys automatically, monitoring that alerts before your users notice, and documentation your team can act on. You own the code and the runbooks at the end of it.
Can you work with an existing AWS account, or do you rebuild from scratch?
Both. Most engagements start with a Well-Architected review of what already exists, producing a prioritised list of security, cost and reliability findings. From there we either harden the current setup incrementally or codify it into Terraform so it can be rebuilt on demand. Rebuilding from zero is a choice, not a prerequisite.
Do you work with startups and small teams?
Yes — small teams often benefit most, because one engineer is usually carrying infrastructure alongside product work. Engagements scale from a one-week monitoring setup to a multi-week platform build.
How do you handle handover and documentation?
Every engagement ends with the Terraform code, pipeline definitions, dashboards and a written handover document in your repository. You can read a sample handover document before committing to anything.
Where are you based and which time zones do you cover?
I work to your clock, not mine. Hours are fully flexible, including permanent evening and overnight shifts, and I cover the full North American range: all US zones from Eastern through Central, Mountain and Pacific to Alaska and Hawaii, and all Canadian zones including Atlantic and Newfoundland. Europe, the Middle East and Africa are a straightforward same-day overlap. Remote by default, in English, French or Arabic.
How can I verify your certifications?
Every credential in the certifications section links to its badge on Credly or to AWS CertMetrics. AWS issues its certification badges through Credly, so the badge page is the issuer's own record — including issue date, expiry and the skills assessed.
// Hire Me
Let's make your infrastructure boring.
Available for DevOps engagements, cloud architecture consulting, data engineering projects — and open to full-time platform roles. Remote by default, working globally. Tell me the problem; I'll tell you the practical next step.
Book a free 30-minute consultation
John-Nessime.com/l/Book-Meeting
Email
john@john-nessime.com
Phone
John-Nessime.com/l/Call
LinkedIn — connect
John-Nessime.com/l/LinkedIn
GitHub — follow
John-Nessime.com/l/GitHub
Medium — follow
John-Nessime.com/l/Medium
Credly — verified credentials
John-Nessime.com/l/Credly