Software and Data Engineering Technology

Modern products are powered by data-driven software.
Applications don’t just run workflows anymore—they learn, predict, and adapt.

To achieve that, two engineering disciplines must work in harmony:

• Software Engineering Technologies → build, scale, and automate core systems.

• Data Engineering Technologies → structure, transform, and deliver insights.

Logiciel unites both disciplines into one seamless framework, connecting data pipelines to applications, and analytics to experience.

Backend Engineering

• Languages: Python, Node.js, .NET, Go, Java.

• Frameworks: Express.js, Django, Spring Boot, FastAPI.

• Cloud & Serverless: AWS Lambda, ECS, Azure Functions, GCP Cloud Run.

• Databases: PostgreSQL, MongoDB, DynamoDB, Aurora.

Outcome: Microservice-driven, resilient systems that scale effortlessly.

Frontend & User Experience Engineering

• Frameworks: React, Next.js, Angular, Vue.js.

• Mobile: Flutter, React Native, Kotlin, Swift.

• APIs: REST, GraphQL, gRPC.

• Testing: Jest, Cypress, Playwright.

Outcome: Fast, interactive, and secure user experiences optimized for performance.

DevOps & Automation Technologies

• CI/CD: GitHub Actions, Jenkins, CircleCI.

• Infrastructure as Code: Terraform, AWS CDK, Pulumi.

• Containerization: Docker, Kubernetes, Helm.

• Monitoring & Logging: Datadog, Prometheus, Grafana, ELK Stack.

Outcome: Continuous delivery with full observability and reliability.

Data Ingestion & Integration

• Streaming: Apache Kafka, AWS Kinesis, Google Pub/Sub.

• Batch Ingestion: Fivetran, Airbyte, Stitch.

• ETL/ELT Tools: dbt, AWS Glue, Apache NiFi, Airflow.

• API Integrations: Custom ingestion via REST or webhooks.

Outcome: Continuous, lossless ingestion from any data source.

Data Storage & Lakehouse Architecture

• Cloud Data Warehouses: Snowflake, BigQuery, Redshift.

• Data Lakes: AWS S3, Azure Data Lake, Delta Lake.

• Lakehouse Engines: Databricks, Synapse, Presto.

• Metadata & Catalogs: Amundsen, DataHub, Glue Data Catalog.

Outcome: A single source of truth—structured, secure, and query-ready.

Data Transformation & Modeling

• ETL Pipelines: Airflow DAGs and Glue Jobs.

• Data Modeling: dbt, SQL Mesh, Great Expectations for quality control.

• Workflow Orchestration: Step Functions, Prefect.

Outcome: Automated, self-healing data workflows that ensure quality and consistency.

Analytics & Visualization

• Visualization Tools: Power BI, Tableau, AWS QuickSight, Looker.

• Query Engines: Athena, Trino, Presto.

• Custom Dashboards: React + Chart.js, Grafana, Metabase.

Outcome: Live dashboards and self-service analytics at every layer.

AI & ML Enablement

• ML Platforms: AWS SageMaker, Vertex AI, Azure ML, Databricks MLflow.

• Feature Stores: Feast, SageMaker Feature Store.

• Data Science Stack: TensorFlow, PyTorch, scikit-learn.

• Automation: MLOps orchestration using Airflow + GitOps pipelines.

Outcome: Production-grade ML pipelines embedded directly into your applications.

• Engineering Excellence: Certified engineers across software, cloud, and data domains.

• AI-Ready Systems: Every build designed to integrate with ML and analytics.

• Faster Delivery: Sprint-aligned execution with measurable milestones.

• Proven Tools, Proven Outcomes: Enterprise-grade reliability at startup speed.

• Security by Default: IAM, encryption, and governance included from day one.