Saber builds data platforms and AI systems that hold up at scale.

Owning enterprise event-driven data infrastructure and orchestration standards for LUMI, a large-scale analytics platform serving petabyte-scale workloads.

• Architected and deployed enterprise-scale event-driven data infrastructure on GCP, leveraging Pub/Sub for real-time message routing with dead-letter queues and idempotent replay, achieving zero data-loss and cutting end-to-end latency from hours to sub-minute resolution.
• Established Apache Airflow as the enterprise orchestration standard on Google Cloud Composer, managing 70+ production DAGs, migrating legacy batch workflows, and eliminating scheduler bottlenecks through advanced monitoring and performance tuning.
• Designed and implemented end-to-end ETL pipelines on Google Cloud Dataflow, processing petabyte-scale datasets and consolidating fragmented source systems into a centralized BigQuery warehouse with unified data models.
• Built and maintained a comprehensive Jenkins-based CI/CD pipeline with automated unit and integration testing, data quality validation, code coverage enforcement, and infrastructure-as-code provisioning across the platform's service fleet.
• Reduced GCP infrastructure costs by automating compute instance provisioning, developing custom Python tooling for high-performance data processing, and driving benchmarking and capacity planning across the platform.
• Accelerated critical backup workflows through automation, reducing processing time by 98% (from 2 hours to under 60 seconds) via optimized pipeline design.
• Standardized AI-assisted development workflows (GitHub Copilot, Claude, ChatGPT) across the data platform team, cutting DAG boilerplate and SQL review time and accelerating delivery.

• Pub/Sub
• Dataflow
• Cloud Composer
• Airflow
• BigQuery
• Spanner
• Jenkins
• Python
• CI/CD

Led backend and data-visualization engineering for a high-concurrency e-commerce and logistics platform.

• Built scalable backend infrastructure supporting 50K concurrent users with zero downtime using Python/Django, covering authentication, order processing, and payment management.
• Developed Java-based data visualization and processing microservices for large-scale file ingestion, ensuring data integrity and efficient parallel processing across platform instances.
• Designed RESTful APIs for inventory, cart, and order management with secure JWT/OAuth2 authentication and integrated payment gateways (Stripe & PayPal), enabling multi-method transactions.
• Implemented real-time asynchronous task processing using Celery and Redis for order confirmation, refunds, and inventory updates, improving system responsiveness and reliability.
• Developed an ML-powered product recommendation engine using browsing and purchase history to deliver personalized suggestions, enhancing engagement and conversion.
• Implemented CI/CD pipelines with Jenkins and GitHub CI for automated testing, building, and deployment.

• Java
• Python
• Django
• REST APIs
• JWT/OAuth2
• Stripe
• PayPal
• Celery
• Redis
• Jenkins

Built an end-to-end ML data platform and high-performance inventory services supporting regional warehouse operations that deliver OIL and GAS to customers across Europe.

• Led and developed a Python-based, high-performance inventory service handling real-time stock synchronization across multiple regional warehouses, reducing inventory sync latency by 60% and preventing overselling incidents.
• Built an end-to-end ML data platform with a Django backend supporting user authentication, multi-format file uploads (CSV, XLSX, Parquet, JSON,...etc), and automated data cleaning, filtering, and transformation.
• Engineered a microservice-oriented architecture with Docker containerization, RESTful APIs for authentication and file processing, and Celery/Redis for asynchronous background tasks — reducing data preparation time by 12%.
• Designed a responsive full-stack interface using HTML, CSS, JS, jQuery, and Ajax, enabling real-time data visualization with Matplotlib, Seaborn, and Plotly and immediate user feedback on uploads and processing.
• Implemented secure data persistence using PostgreSQL/MySQL for user metadata and file information, with Django session management and dynamic template rendering.

• Django
• Python
• Docker
• PostgreSQL
• MySQL
• Celery
• Redis
• Plotly
• Matplotlib
• Seaborn

Architected an ML-powered pipeline monitoring platform for real-time sensor-driven decision-making in energy infrastructure.

• Architected an ML-powered pipeline monitoring platform using Flask, Python, and ML libraries (scikit-learn, TensorFlow), improving decision-making accuracy by 20% over manual sensor monitoring with real-time predictions.
• Led platform adoption by providing comprehensive training and documentation, enabling field engineers and technicians to assess pipeline health independently.
• Built scalable backend infrastructure with RESTful APIs, Celery/Redis for asynchronous processing, and Redis caching to handle large-scale datasets while maintaining sub-second response times.
• Implemented a comprehensive data processing pipeline using NumPy and Pandas for feature engineering, coupled with ML models to classify pipeline conditions (water saturation vs. oil readiness) from real-time sensor data.
• Established CI/CD infrastructure using Jenkins/GitLab CI with automated testing and deployment, containerizing the application with Docker for consistent dev-to-prod on AWS (EC2 & S3).

• Flask
• Python
• scikit-learn
• TensorFlow
• NumPy
• Pandas
• Docker
• AWS EC2
• AWS S3

Built a real-time chat and messaging platform with secure authentication and high-throughput infrastructure.

• Built a real-time chat application using Flask, Flask-SocketIO, and WebSocket technology, enabling low-latency bidirectional communication with secure JWT-based authentication.
• Designed scalable backend infrastructure with AWS RDS MySQL for message storage, Redis for session management and message brokering, and S3 for media serving — supporting high-throughput chat operations.
• Implemented Firebase Cloud Messaging for real-time push notifications on new messages and events, enhancing engagement and in-app responsiveness.
• Secured the platform with Cloudflare for DDoS protection and AWS CodePipeline with integrated logging and monitoring via AWS CloudWatch for proactive issue detection and optimization.

• Flask
• Flask-SocketIO
• WebSocket
• AWS RDS
• Redis
• S3
• Firebase
• Cloudflare
• AWS CloudWatch