Software Development | Data Engineering

> Web Development

Cloud Native Applications & Microservices

> Web Applications

Use Cases

Enable seamless access to data-driven tools for logistics, inventory, and AI-powered recommendations with modern, responsive interfaces tailored for business needs.

Description

Web applications are built using TypeScript, Alpine.js, and TailwindCSS for fast, scalable, and maintainable frontends. Astro is used as the primary framework, ensuring optimal performance. These applications are designed for intuitive user experiences and are integrated with backend systems for real-time data processing.

> API Development

Use Cases

Facilitate integration between internal systems and third-party platforms, enabling data flow and automation in supply chain and logistics workflows.

Description

APIs are developed using FastAPI and Pydantic for robust, type-safe, and high-performance endpoints. These APIs support microservices architectures and are designed for scalability, security, and ease of integration with external tools and internal data pipelines.

> CI/CD & IaC

Use Cases

Ensure rapid, reliable, and repeatable deployments of software systems to reduce time-to-market and maintain high-quality standards.

Description

CI/CD pipelines are built using GitLab and Docker, with deployments managed via Docker Compose and Kubernetes. Infrastructure is defined using declarative configuration files and version-controlled in Git. Automated testing and code analysis are integrated using tools like mypy and pytest and ESLint.

> Data Engineering

Data Integration & Data Pipelines

> Data Integration

Use Cases

Unify data from multiple sources such as ERP systems, APIs, and databases to create a single source of truth for operational efficiency.

Description

Data integration involves connecting disparate data sources—such as databases, APIs, and cloud services—using ETL (extract, transform, load) processes. Techniques include API orchestration, message queues, and event-driven architectures. Tools like FastAPI, SQLAlchemy, and Pydantic ensure robust and type-safe data handling for scalable and maintainable systems.

> Data Pipelines

Use Cases

Automate the flow of data from ingestion to transformation and storage, ensuring consistent and reliable data for analytics and machine learning models.

Description

Data pipelines are built using modular, lightweight tools to process and transform data efficiently. Techniques include batch and stream processing, data validation, and orchestration using custom tools and Pydantic for schema enforcement. Python libraries like Polars, PyTorch and Numpy enable efficient and reliable data processing workflows.

> Data Warehousing

Use Cases

Store and organize large volumes of structured and semi-structured data for efficient querying, reporting, and advanced analytics across business functions.

Description

Data warehousing involves designing and maintaining optimized relational and NoSQL databases for analytical workloads. Techniques include schema design and indexing. Technologies used include PostgreSQL, SQLite, and cloud storage systems. Data is queried using SQL and processed with Python tools like SQLAlchemy and Polars for performance and flexibility.