Akram Zioud

- Definition of business and technical needs in direct collaboration with the Product Owner and developers;

- Translation of needs into technical solutions (SQL queries, Scala code, Python, etc.);

- Writing User Stories and acceptance criteria to meet business needs.

- Analysis of sales data from the website, application, and partners using complex SQL queries (PostgreSQL);

- Analysis of Scala code (Glue jobs) to extract business management rules and transcribe them into SQL/Athena for DBT;

- Validation of data integration by monitoring volumes and rejections in CloudWatch;

- Monitoring and analysis of Airflow DAGs that execute AWS Glue and EMR jobs via CloudWatch to detect and diagnose anomalies;

- Control of data quality in DWH, Datamart, and Datalake environments;

- Analysis and integration tests related to data evolution;

- In-depth diagnosis of recurring anomalies and identification of causes;

- Monitoring of runs and data recovery via EMR Recovery.

- Implementation of net annual sales calculation using a complex SQL query;

- Design and deployment of a new feature in the Datalake, including the creation of a Glue job (Scala) and an Airflow DAG;

- Participation in the sales data redesign project;

Modeling tables to optimize data structuring;

In-depth data analysis to understand and adapt inbound flows using SQL queries;

- Validation by volume tests to ensure data quality and reliability;

- Participation in the "My SNCF Year" project with functional transformation and validation. Modification of Scala code to SQL for acceptance testing.

Active participation in the design, optimization, and production deployment of data processes aimed at ensuring the quality, reliability, and valorization of business data throughout their lifecycle.

Process Data Quality & Integration Workflows

- Design and implementation of data quality workflows to ensure the normalization, cleaning, and standardization of customer and business data, thereby ensuring their usability in downstream systems;

- Implementation of validation, deduplication, and data standardization rules to correct inconsistencies, remove duplicates, and make repositories reliable;

- Execution of functional and technical acceptance tests, monitoring production deployment of processes, and validating their proper functioning;

- Corrective and evolutionary maintenance of ETL processes to integrate business and technical changes;

- Scheduling and monitoring of jobs via Jenkins, with the implementation of reporting and monitoring to ensure traceability and performance of processes.

Enrichment and valorization of internal data

- Processing, transformation, and automation of data enrichment processes to increase their added value and improve their use in business analyses;

- Integration of new data sources and creation of automated workflows to feed internal systems with enriched, normalized, and cleaned data.

Evolution and optimization of matching tools

- Complete redesign of inbound file diagnosis mechanisms to quickly identify anomalies and improve their processing;

- Harmonization of normalization processes and optimization of matching rules to improve the accuracy of correspondences between source and target data;

- Implementation of more robust management rules to reduce matching errors and increase the reliability of

Participation in the operation, quality, and reliability of data within the data team. Performing file reconciliation processes using dedicated applications and SQL queries, monitoring and reporting on business requests, and correcting data quality anomalies. Contribution to testing and acceptance phases for data aspects and design of decision-making dashboards in Power BI for key indicator monitoring.