Engineering Manager (W/M)

Job Description

At Lesaffre, Digital, Data & Tech (DDTech) is a fundamental transformative force for Lesaffre growth. It is a lever to increase customer centricity and user experience, achieve operational excellence, supercharge R&D, and empower our teams across the entire company.

At the heart of this digital transformation is our delivery department, where we turn innovative ideas into impactful solutions. Central to that effort are our Solution Engineering squads, the Data Engineers, Software Engineers, and Web Engineers who build and ship the products our business relies on.

As our delivery scales, the way we engineer and the way we grow people must evolve together. We need a leader who can both raise the technical bar and grow the engineers, while embracing a new generation of AI-assisted and agentic engineering practices that are reshaping how software teams are structured and how they deliver.

In this context, Lesaffre is recruiting an Engineering Manager.

As the Engineering Manager, you wear two hats: you are a technical leader who stays close to the code, the architecture, and the quality of what we ship, and you are a people leader who grows engineers, shapes team structure, and allocates talent to the Group's priorities. You are fluent in modern agentic engineering practices (AI coding assistants, agent-driven workflows, automated review and testing) and you understand how they change the way engineering teams are organized, sized, and led. Technical credibility is essential: you cannot raise the bar or grow engineers without it.

Key Responsibilities:

Team Leadership & People Management

• Grow the Engineers: Manage, mentor, and develop the engineers of the Solution Engineering squads (Data, Software, Web), building clear career paths, upskilling plans, and a fair, structured performance process.
• Staffing & Allocation: Own the dynamic staffing and allocation of engineers across the Product Squads, balancing delivery commitments, individual growth, and operational support as priorities shift.
• Culture Builder: Foster a "One Team" engineering culture grounded in software craftsmanship, ownership, automation-first thinking, and continuous learning.
• Hiring: Own the technical hiring process end to end with HR, defining role profiles, building the pipeline, and running interview loops.

Technical Leadership & Engineering Excellence

• Hands-On Credibility: Stay close to the code and the architecture. Contribute directly to design decisions and code reviews to guarantee secure, robust, and maintainable solutions.
• Define the Bar: Define, document, and champion engineering best practices, coding standards, code review, testing, CI/CD, observability, and documentation.
• Golden Paths: In partnership with the Lead Cloud & Solution Architect and the Platform Engineering Manager, promote reusable patterns and "Golden Paths" that make the right way the easy way.
• Engineering Health: Define and track engineering effectiveness metrics (e.g., DORA: deployment frequency, lead time, change failure rate, MTTR) and use them to drive improvement, not surveillance.

Agentic Engineering Practices

• AI-Augmented Development: Lead the adoption of AI-assisted and agentic engineering workflows, defining how the team uses AI coding assistants, agent-driven automation, code generation, automated review, and test generation to increase quality and velocity.
• Impact on Team Topology: Anticipate and shape how agentic practices change the way engineering teams are structured, sized, and staffed, and adapt squad organization and roles accordingly.
• Guardrails & Governance: Establish responsible-use guidelines for AI in engineering: security, IP protection, data confidentiality, dependency hygiene, and human-in-the-loop review of AI-generated code.
• Enablement & Evidence: Roll out the tooling and enablement (training, playbooks, prompt patterns) that make the team genuinely productive, and track the impact on quality, throughput, and developer experience.

Delivery, FinOps & Run Stability

• Reliable Delivery: Guarantee the resilience, maintainability, and quality of the applications the squads deliver, balancing new delivery with the deliberate reduction of technical debt.
• Applicative FinOps: Build cost awareness into engineering practices, sizing Cloud (AWS/Azure) and Data (Snowflake) resources to real usage and reducing waste through direct technical contribution.
• Reduce External Dependency: Strengthen internal engineering capability to reduce reliance on external providers where it makes sense.

Financial Management

• Engineering Cost Ownership: Contribute to the budget for your engineering resources, steering the internal-vs-external mix of your engineers to deliver against the roadmap cost-effectively.
• Engineering Cost & Value: Track the cost and value of the engineering work your teams produce, and feed applicative FinOps insights back into technical and staffing decisions.
• Investment Cases: Build and defend business cases for engineering investments, in particular agentic tooling, AI assistants, and upskilling, quantifying expected productivity and quality gains.

Performance Measurement & Reporting

• Engineering Metrics: Define and maintain engineering effectiveness metrics (DORA: deployment frequency, lead time, change failure rate, MTTR) alongside code quality and delivery indicators.
• Agentic Impact: Measure and report the impact of AI-assisted and agentic practices on throughput, quality, and developer experience.
• Leadership Reporting: Report progress, risks, and achievements to the Head of Engineering & Infrastructure through dashboards and regular reviews, translating technical results into business value.

Qualifications

Required Skills & Experience:

• Experience: 10+ years in software, Cloud, or Data engineering, with a proven track record combining hands-on technical leadership and people management (Tech Lead, Tech Lead Manager, or Engineering Manager).
• Technical Legitimacy: Strong background as an engineer, tech lead, or architect across Cloud (AWS and/or Azure) and modern software/DevOps practices.
• Data Ecosystem: Practical experience with modern data environments (Snowflake or equivalent): pipelines, modeling, and governance.
• Agentic Practices: A credible, practical point of view on AI-assisted and agentic engineering, and on how these practices reshape engineering team organization.
• Agile Leadership: Proven practice of Agile methodologies (Scrum, Kanban) in a product-oriented delivery context.
• Engineering Quality: Strong culture of software quality (Clean Code, TDD, code review, observability) and applicative FinOps.
• Communication: Excellent interpersonal skills, able to bridge deep technical teams and business stakeholders and to lead through influence and trust.
• Languages: Fluent in English and French (written and spoken) is mandatory for local collaboration and global documentation.

Nice-to-Have Skills :

• Experience introducing AI coding assistants or agent-based workflows into a production engineering team.
• Background as a hands-on Software Engineer, Data Engineer, or DevOps/SRE.
• Exposure to product-oriented operating models ("you build it, you run it", Team Topologies).
• Understanding of secure-by-design and applicative FinOps principles.

There is no such thing as a ‘perfect’ candidate. Lesaffre is a place where you can grow and in which whatever background you come with will be a plus. If you feel excited about the position, we strongly encourage you to apply even if you meet only two thirds of the requirements.

Additional Information

What can Lesaffre offer you:

• We are a successful family-owned company with long-standing history where people truly matter.
• We promote a sense of fulfilment with a genuine mission: nourish and protect the planet.
• An opportunity to work in a real international environment.

All our jobs are open to people with disabilities.

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