Meryll Levet

- Development of APIs in FastAPI

- Application of architectural designs: microservice and clean architecture

- Management and optimization of the Snowflake database and creation of data models.

- Implementation of best practices (code, tests, CI/CD) and agile processes to ensure quality and efficiency of deliverables.

- Resource management

- Deployment of Service Bus (topic mode)

- Deployment of API Management

- Deployment of Azure Functions

- Deployment of APIs on the Snowflake cloud

- CI/CD pipeline management

- Application of SRE principles.

Real-time customer management (previously batch)

Increased reliability of the customer repository by 40%

Reduced processing errors by 20%

- Team Supervision: Daily management of the Data team, responsible for the technical quality of deliverables.

- Technical Leadership: Guiding Data Engineers on development best practices, standardizing the codebase (linting, tests, reviews, continuous integration).

- Project Responsibility: Design, development, and optimization of Data Platform architectures to meet needs.

- Optimization: Optimizing infrastructure costs while ensuring data security, compliance, and governance.

- Infrastructure Industrialization: Design, deployment, and maintenance of a hybrid Data platform (on-premise and GCP), managing infrastructure as code (Terraform, Ansible).

- Automation: Mastery of containerization, implementation and management of CI/CD pipelines for effective continuous integration.

- Operational Maintenance: Adherence to SRE best practices (monitoring, alerting, SLOs) to ensure the reliability and security of production systems, including incident management.

- Pipeline Development: Design and development of data pipelines (batch and streaming) on Kafka, Dataflow, BigQuery, Cloud Functions, and other GCP technologies.

- Cost and Performance Optimization: Continuous improvement of transformation pipelines and their performance.

- Data Management: Management of OLAP and OLTP databases (BigQuery, Postgres, SQL Server) and data models to ensure performance and scalability.

Impact: Transformation of a major project with successful cloud migration, cost and performance optimization, and ensuring the reliability of production platforms.

- Pipeline Development: Design and development of data pipelines (batch) for ML algorithms

- ML Solution Deployment: Development and production deployment of advanced algorithms: CNN, LSTM

- Operational Maintenance: with production incident management

- Automation: Implementation of continuous integration to ensure the quality and reliability of deliverables.

- Software Development: Design and development of code for embedded systems with an "event-driven" architecture

- Deliverable Quality: Implementation of development best practices within the team (linting, testing, review).

Impact: Improvement of user experience on music recommendations offered by the product, leading to a 10% increase in NPS (Net Promoter Score).