AI Data Engineer F/H/X

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.

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 privilege, and work with Security and Infrastructure teams to enforce them.

Design and run data reconciliation and dual-run cutover processes: running the legacy and target systems side by side against the same data, to prove the migration is correct before the legacy system is retired.

Build and enforce data-quality controls (validation rules, anomaly detection, completeness and consistency checks) as an automated, repeatable part of every migration.

Apply AI, including large language models (LLM), to migration‑specific problems: proposing a high-level data mapping between source and target schemas, drafting a migration test plan, and flagging likely anomalies for human review. Build the data pipelines and retrieval layer an AI‑assisted mapping tool needs to reason over a legacy schema well: turning table and column definitions, constraints, and business documentation into a form a model can retrieve from accurately, and keeping that data clean, consistent, and current as the migration itself changes it.

Package data‑processing components as containerized services with a documented application programming interface (API) and contribute them to Teamwill’s shared asset portfolio for reuse across engagements.

Work closely with Business Analysts, Architects, Software Engineers, Platform Engineers, and client‑side data owners to sequence a migration safely, including on live, regulated systems.

What You’ll Work On

You will work on data migration and modernization engagements for large-scale, regulated organizations in financial services. A representative engagement: a legacy system where part of business rules are embedded as stored procedures directly in the database, invoked from multiple call sites across dozens of services, which needs to move to a modern architecture without a single point of downtime and without losing an audit trail. You will also apply AI to accelerate the mapping, planning, and testing phases of this kind of work, in a production context where data residency, regulatory approval, and compute capacity shape solutions.

The work is international: you will collaborate with teams and clients across Teamwill Consulting’s geographies.

Job requirements

What We’re Looking For

Master’s degree (or equivalent professional experience) in computer science, data engineering, or a related field.

3+ years of hands‑on experience building production data pipelines, including at least one large‑scale data migration or platform modernization project.

Strong Python and Structured Query Language (SQL) skills; comfort writing tests for data pipelines (for example with PyTest) and treating data‑quality checks as code.

Practical experience with schema as code and versioning tooling (for example Liquibase or an equivalent) and with relational databases at production scale (for example PostgreSQL).

Working knowledge of containerization (Docker) and container orchestration (Kubernetes), and of continuous integration / continuous delivery (CI/CD) pipelines.

Comfort working with AI tools, including large language models and AI coding agents, as part of an engineering workflow (for example for automated mapping proposals, test‑plan drafting, pipeline code, and migration scripts), keeping a human accountable for migration decisions and applying review discipline (automated checks plus human review) to agent‑produced code as to anyone else’s.

Software craftsmanship instincts and an Everything as Code mindset: pipelines, schemas, data‑quality rules, and documentation are versioned, reviewable, and automated.

Professional fluency in English; professional French for roles based in France or Tunisia.

Nice to Have

Experience extracting business logic from a procedural database language (for example PL/pgSQL) into a modern application language, at scale, ideally in banking, capital markets, or another regulated financial‑services sector.

Familiarity with event‑driven architecture (for example a pattern such as Kafka and Debezium) and with change‑data‑capture (CDC) techniques for near‑real‑time data synchronization, useful for services that must be decoupled during a migration.

Familiarity with the Model Context Protocol (MCP), a standard for connecting AI agents to external tools and data sources.

Location & Mobility

A minimum of 3 days per week, on site is expected, at Teamwill Consulting offices or at client engagement locations, with travel varying by assignment.

Teamwill fosters an inclusive work environment and ensures equal opportunity in all hiring decisions, regardless of gender, age, national or ethnic origin, disability, sexual orientation, gender identity or expression, beliefs, or any other characteristic protected by applicable law.