This assignment involves the implementation of a company repository intended to become the reference source for identifying companies within a public organization. Gradually fed by federal and regional sources, this repository aims to reduce data dispersion and duplication, enhance its quality and reusability, and support the "Only Once" principle. The mission adopts a "quality by design" approach, where data quality is integrated from the ingestion, integration, and exploitation phases.
What you do
- Define, implement, and continuously improve processes for data integration, quality control, inconsistency detection, and data remediation.
- Design processes for feeding, transforming, normalizing, and quality controlling the repository.
- Define mechanisms for generating and managing a unique identifier, deduplication, consolidation between sources, and data conflict management.
- Implement integration chains, connectors, and data exchange flows, ensuring their complete traceability and adherence to existing architectural and sharing standards.
- Define and automate quality controls applied during ingestion and on existing data: format validation, completeness, temporal consistency, cross-source validation, and uniqueness.
- Design mechanisms for detecting anomalies and inconsistencies, as well as alerts, quality indicators, dashboards, and monitoring reports.
- Develop data cleansing and remediation processes: standardization, normalization, deduplication, enrichment, and correction of detected errors.
- Establish a permanent system for monitoring data quality and tracking indicators such as accuracy, completeness, consistency, uniqueness, freshness, and compliance.
- Analyze trends and propose technical or organizational improvements.
- Contribute to the operational exploitation of the repository, the resolution of technical incidents related to data, and the documentation of processes and tools.
- Analyze the technical and functional impacts of gradual connections and participate in the definition of test scenarios and integration campaigns.
- You will join a team composed of a project manager, a technical coordinator, and an analyst.
Profile
- Confirmed experience as an ETL / ELT Data Engineer.
Technical skills.
- Good knowledge of data governance principles (DAMA, etc.).
- Theoretical and practical knowledge of ETL tools and data quality analysis tools, notably FME and existing Talend standards.
- Proficiency in processes for analyzing and designing database management systems.
- Theoretical and practical proficiency in Data Quality Management processes.
- Ability to design, test, and implement reliable integration and quality control processes.
Soft Skills.
- Dynamic, flexible, and pragmatic.
- Good communication, assertiveness, and active listening.
- Service orientation and ability to understand the needs of data managers, including in urgent situations.
Languages.
- Excellent command of French, both oral and written.
How to apply
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