Nour Jaouadi

Project 1: Retrieval of banking data (loans, third parties, etc.) requested by the SG group for Reporting and cross-functional applications, in compliance with the new banking regulations imposed by the ECB (European Central Bank).

- Development of new management rules: Understanding business needs, developing solutions, functional testing on data, optimizing jobs, performing deployments.

- Monitoring of monthly production: Monitoring job launches, querying and verifying produced data, automating data checks, managing and resolving production anomalies.

Project 2: Migration of a project based on internal databases to the DataLake. This allows for extractions based on a larger and more complete history from the DataLake. These extractions represent unpaid operations for the securitized debt scope.

- Collaboration with the functional team to understand the need and ensure the migration solution meets the request.

- Development of the project from scratch on Scala/Spark to source data from the DataLake. Creation of jobs on Control-M for monthly production.

- Comparison with the current production project (on SQL) to validate the new solution and deploy it to production.

Spark, Scala, SQL, Hive, Hadoop, Control-M, Jenkins, Git, JIRA

Accelerating the data cleaning industrialization process to ensure good data quality for Data Scientists for project modeling, in an international context.

- Building cleaners to clean and harmonize data.

- Participation in the industrialization process of cleaners for different countries on Azure Data Factory: Optimizing processing with PySpark, adding sanity checks on input and output data.

- Development of a Python package containing data transformation functions to accelerate the development and industrialization of cleaners for different countries: CI/CD pipeline on Azure, unit tests, MkDocs documentation.

- Development of a Python package to build SQL queries executable on Snowflake. This tool automates team requests, makes them configurable, and facilitates development with better notebook readability.

Python, SQL, Pyspark, Snowflake, Databricks, Azure DevOps, MkDocs

Migration of the anti-terrorism financing (FT) alert generation engine, already in production on SAS, to a Big Data environment (in collaboration with financial security).

- Monitoring, improvement, and maintenance of the FT engine production on SAS pending migration.

- Migration of the FT engine to a Hadoop DataLake environment: Design of the new solution, PySpark development, job scheduling via Airflow, comparative acceptance testing.

- Development of the FT engine for legal entities (Associations, SMEs, etc.).

Python, Spark, Hive, Apache Ambari, Apache Airflow, Hadoop, SQL, Bitbucket, SAS, Git