Hi, I'm
Amin Al-Ait
Cloud Data Engineer
About
Cloud Data Engineer with 4+ years of hands-on AWS experience, most of it on serverless data pipelines for automotive test data: proprietary formats like ATFx, ATFx-NVH, MF4 and Modal alongside standard tabular data. I contributed to all seven of the platform's ETL Terraform modules, helped cut analysis costs by over 50% and time-to-result by over 75%, and owned the testing strategy across the full pyramid, from pytest unit tests to golden-file end-to-end runs against live AWS.
I ran the benchmarks the platform's storage architecture was chosen on (a 175-run InfluxDB-versus-Athena comparison, a 20-variant Parquet encoding study), led a proof of concept digitising vehicle test reports with AWS Textract, and wrote the team's dependency-vetting and Docker base-image vetting standards. I also migrated and maintained the production Grafana estate across InfluxDB Flux, Athena SQL and Prometheus.
Alongside client work I build QGI, a solo, seven-repository AWS platform that reads a country's multi-decade indicator trajectory against the historical record and ranks the past cases it most resembles: 250 indicators from 8 providers, over 10,000 verified events spanning 1903 to 2026. It runs on a multi-account AWS backbone with Step Functions, Batch on Graviton Spot, and a 14-billion-row Parquet lake, cross-validated throughout, leave-one-country-out and a fleet-wide integrity audit. Full breakdown on the QGI project page.
Skills
Cloud & Infrastructure
- AWS
- Terraform (IaC)
- AWS Lambda
- AWS ECS
- AWS S3
- AWS API Gateway
Data Engineering
- SQL
- ETL Pipelines
- AWS Athena
- AWS Glue
- Parquet / PyArrow
- InfluxDB
- Data Mining (Cross-Correlation & Pattern Discovery)
- Dashboards (Grafana / Streamlit)
Development & Testing
- Python
- Java
- Backend Development
- ETL Testing / QA
- Pyramid Testing
- CI/CD (GitLab)
- Monitoring & Observability
Platforms & Tooling
- GitLab
- cplace (No/Low/Pro-code)
- Agile / Scrum
- GenAI-assisted Dev
AI / ML & Data Science
- XGBoost / Gradient Boosting
- scikit-learn
- Feature Selection & SHAP (Shapley additive explanations)
- Model Validation (LOCO / Backtesting)
- Statistical Learning
- Time-Series & Correlation
Experience
Cloud Data Engineer
demicon, Automotive Enterprise
Automotive
Helped build and deliver a cloud-native data analysis platform for an automotive client, replacing a manual Excel-based process. The platform reduced analysis costs by >50% and time-to-result by >75%.
- Engineered, maintained, and monitored multiple ETL pipelines that processed large datasets with secure, reliable transformations.
- Conducted structured requirements elicitation, in English, with client stakeholders to characterise source data formats and business rules, then designed and delivered the corresponding data pipelines.
- Implemented a full ETL Testing Strategy covering the entire Testing Pyramid.
- Managed infrastructure deployment via Terraform across AWS Lambda and ECS.
- Verified end-to-end data integrity from source to persistence layer.
- Built an automated compliance tool that audits every third-party dependency across the stack (Poetry, npm, yarn, yum) for the copyright and licence artefacts its licence requires, raising licence compliance from 65% to 98%.
- Used privacy-compliant GenAI and LLM tooling daily across the data-engineering workflow: reverse-engineering undocumented binary formats, generating and refactoring pipeline code, and speeding up exploratory data analysis.
AWS Cloud Developer
goes faster OÜ
Sports Tech
Contributed to a mobile running coaching application, building the entire backend from scratch in a small, agile team alongside a professional runner and a frontend developer.
- Developed and owned the complete backend for the running coaching app.
- Implemented GPS data analysis to assess athlete pace and geolocation performance.
- Designed a scalable, cost-effective serverless architecture on AWS.
Certifications
CLF-C02
AWS Certified Cloud Practitioner
Amazon Web Services
DEA-C01
AWS Data Engineer Associate
Amazon Web Services
Education
2020 – 2023
M.Sc. Data Science (Coursework completed, degree not awarded)
TU Dortmund University
2016 – 2019
B.Sc. Computer Science
Lebanese International University
Languages
Projects
QGI: Quantitative Geopolitical Intelligence
Active DevelopmentAn applied-ML platform that spots which countries are tracing the trajectories that preceded past crises, and ranks the historical precedents behind each signal.
A solo-built, seven-repo AWS system. Its current methodology, the Trajectory Engine, is a fleet of 42 per-event-type models (logistic regression or XGBoost) cross-validated leave-one-country-out, and ranking today's countries by resemblance to historical precedents. Live at qgintelligence.com; full breakdown, including the platform's retired first-generation engine, on the projects page.
Full project breakdown ↗Contact
Whether you're interested in what QGI patterns reveal about your region or sector, want to discuss a cloud data engineering engagement, or are looking to recruit, reach out.