Teamwill
Publiée il y a 6 jours · Courbevoie
AI Data Engineer F/H/X
- Courbevoie
Description du poste
About Teamwill Consulting
Putting domain expertise and artificial intelligence at the heart of the decisions that finance millions of projects, vehicles, and equipment around the world is the core of what we do.
For more than 20 years, Teamwill Consulting has been supporting leading players in banking, consumer finance, and mobility through their strategic and technological transformations. Operating in 11 countries and bringing together more than 800 professionals, the Group combines deep industry expertise, close client relationships, and the ability to deliver on a global scale.
Artificial intelligence is a cornerstone of our growth strategy. To carry that ambition, we are strengthening our technology, engineering, and data teams.
Role Summary
Teamwill Consulting’s Data Migration & Quality practice moves clients from legacy data platforms to modern, target architectures without losing trust in the data along the way: mapping, transformation, reconciliation, and lineage. As an AI Data Engineer, you build the pipelines and tooling that make that move safe and auditable, and you apply artificial intelligence (AI) to the parts of a migration that are normally the slowest and most error-prone, including high-level mapping, anomaly detection, and planning.
Key Responsibilities
- Design and build data pipelines that extract, transform, and load data from legacy sources into a target platform, including moving business logic that lives inside a database (for example stored procedures) into versioned, tested application code. Build and maintain the ingestion and monitoring scripts, with clear logging and traceability, so a data issue in production can be traced back to its source.
- Own and implement schema evolution and versioned database migrations, including moving from a shared database design to one schema, and one database, per service. Define accesses, rules, and controls for data, databases, schemas, and tables, applying the principle of least p
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