Jonathan Ndamba

Participation in the development of the GCP Data Platform for the IT Europe CRM team

Mission within the IT Europe CRM team to develop:

° Data ingestion flows

° Data exposure flows

° KPI and segmentation calculation

° Ingestion, transformation & exposure pipeline

° Technical documentation

° Deployment scripts

Environment:

° Google Cloud Platform

° Sql

° Python

° Terraform

° CICD

° Bash

° FastApi

° Decision Support - Business Intelligence

° Real-time

° Functional domains

° CRM - Customer Relationship Management

° English

As part of a project to redesign the entire decision-making chain for the E-voyageurs group, I am assisting Oui.sncf in migrating an on-premise datalake to an AWS datalake. The objective and challenge of this project is to move away from the historical on-premise foundation to implement a new, more agile and innovative platform by leveraging cloud services.

Mission:

° Development and design of elegant and simple code for high-performing, reliable, and maintainable architectures and applications.

° Implementation and maintenance of the CI/CD chain.

° Design of infrastructure as code deployable from the CI/CD stack.

° Development of Python scripts/modules for triggering events via AWS Lambda.

° Implementation of tests and coverage across the entire software architecture.

° Implementation and maintenance of Kafka code.

° Creation and management of storage solutions on AWS (S3 and Dynamo).

° Designing and deploying the stack in Typescript (Back-end) using CloudFormation & CDK.

° Development in an agile environment.

° Implementation and maintenance of an API via AWS API Gateway for managing access to data and resources for employees.

Environment: AWS(S3, Gateway, Secret Manager, IAM, Dynamo, CDK), Python, Kafka, Typescript, Pytest, Gitlab, Windows, IntelliJ, Vscode, Docker, Kubernetes

Assisting clients in implementing their data-driven marketing strategy. These steps include routing data into the internal datalake (Cloud Composer | Airflow / Cloud Storage), then making it available in the data warehouse (BigQuery), followed by data cleaning (Dataproc/Spark), and finally data analysis and modeling (AI Platform).

Missions:

o Define a data ingestion pipeline using an ETL/DAG (Airflow) and clean data.

o Deploy visualization tools using Streamlit and App Engine (GCP) to aid decision-making.

o Implement a marketing mix model using an econometric methodology combined with Bayesian regression.

o Deploy the packaged solution in CI for automating various marketing mix modeling tasks.

o Define a market basis analysis for optimizing promotional campaigns.

o Train clients on various data science and MLOps methods.

o Implement unit tests with pytest and the TDD methodology.

o Implement best practices within the data science team and conduct code reviews.

o Write documentation using Sphinx.

Environment: GCP, Windows/Linux, GitLab CI, Python, Spark, Git, SKlearn, pandas, Dask, pytest Github, VBA, EXCEL