Data Engineering Manager (AWS) - ML & Analytics

• Paris
• Permanent Contract
• Fulltime

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We are partnering with a high-growth European tech platform operating at significant scale, building a product driven by real-time interactions and direct monetization.

With millions of users and a high volume of daily transactions, data sits at the core of both product innovation and business performance.

About the role :

As Head of Data, you will lead the data strategy end-to-end, acting as both a technical leader and business partner . You will own the full data scope from platform architecture to advanced Machine Learning use cases while managing a small, high-impact team.

You will work directly with executive leadership and product teams to turn large-scale data into actionable insights and intelligent features.

Responsibilities :

Data Platform & Architecture

• Define and scale a modern AWS-based data platform (Lakehouse archi
• tecture)Ensure data quality, reliability, and performance across pipelines
• Own tracking strategy and event-based datamodeling

Machine Learning & Advanced Analytics

Lead development of:

• Recommender systems
• Predictive models (churn, monetization, engagement)
• Advanced experimentationframeworks
• Drive innovation using Deep Learning &GenAI

Business Impact & Analytics

• Translate complex data into clear, actionable business insights
• Influence product and growth decisions through data
• Own key metrics: revenue, retention, LTV, acquisition performance

Leadership

• Manage and mentor a small team of Analytics & ML
• Engineers (≤4)Build strong data practices (SQL, DBT, code review, xuezdbg experimentation)
• Foster a data-driven culture across the organization

Performance & FinOps

• Monitor and optimize AWS data costs
• Ensure scalability and efficiency of data pipelines

About you :

• Experience leading Data / ML teams in high-scale environments
• Strong hands-on expertise:Python, SQLAWS ecosystem (Redshift, S3, etc.)
• Modern data stack (DBT, BI tools)
• Proven experience with: Recommender systems or predictive modeling
• Experimentation (A/B testing)Ability to connect technical topics with business impact