VP DATA
About Mirakl: Founded in 2012, Mirakl has been at the forefront of marketplace innovation, empowering every business to compete in the platform economy. Today, Mirakl’s operating system combines an enterprise marketplace solution (Mirakl Platform) that enables retailers and B2B organizations to launch, scale, and operate marketplaces and dropship, AI-powered multichannel selling (Mirakl Connect), retail media (Mirakl Ads) and an agentic commerce infrastructure (Mirakl Nexus). With dual headquarters in Boston and Paris, Mirakl helps a global ecosystem of 450+ marketplaces (B2C and B2B) and a network of over 100k third-party marketplace sellers. Brands like Macy’s, Decathlon, Carrefour, Asos, and Airbus Helicopters use Mirakl to grow their businesses in new and remarkable ways. For more information: . Mirakl in Numbers:
- Founded in 2012 | Member of French Tech Next40
- 750+ employees in 9 offices worldwide: Paris, Barcelona, Bordeaux, Boston, London, Munich, New York, Sydney, Tokyo
- 350+ Mirakl Tech teams members mainly based in France
- 5 Saas Solutions
- Work Hard Together: Teamwork and collaboration are the foundation of our success
- Get Things Done: We prioritize action and efficiency for impactful results
- Go Above & Beyond: We tackle challenges proactively and always aim for excellence
- Succeed Through Expertise: Knowledge sharing and continuous learning are core to our culture
- Satisfy & Empower Clients: We're committed to our clients' success
- Scale and manage Data teams across platform, analytics, and AI foundations
- Define and execute the Data, AI, and Agentic Foundations strategy
- Own a budget (infra, tooling, scaling)
- Drive alignment across AI, Product, Engineering, and Business
- Ensure execution, prioritization, and delivery at scale
- Deliver a scalable, secure, governed data platform (100s of TBs)
- Structure data raw → silver → gold for analytics and AI
- Ensure data quality, reliability, observability, and security
- Align data models with core business domains (revenue, ops, product)
- Build a best-in-class AI development platform
- Provide Data Scientists, Agent Builders, and AI Engineers with tooling for:
- Model development, testing, deployment
- Experimentation at scale
- Operate robust model serving / inference (trillions of tokens/year)
- Ensure performance, monitoring, and cost control
- Development (frameworks, integrations)
- Execution (orchestration, tools, memory) and Monitoring
- Evaluation (metrics, testing, feedback loops)
- Standardize agent lifecycle, safety, and reliability
- Deliver analytics for internal and product use cases
- Build and scale analytics agents (internal & in-product)
- Build data, semantic, and context layers that are consistent, reusable, and agent-ready
- 10–15 years in data, AI, or platform organizations, leading high-performing cross-functional teams (platform, analytics, AI)
- Proven track record building data platforms and analytics systems that drive business decisions and power product experiences
- Experience delivering data and analytics products (semantic layers, metrics, business-facing models)
- Strong understanding of data + AI ecosystems (analytics, LLMs, agents) and their business impact
- Experience operating production systems in cloud environments, in close collaboration with MLOps and AI/agent teams
- Build production-grade platforms and organizations that scale
- Connect data, AI development, and serving into one system
- Balance performance, cost, security, and business impact
- Drive execution with high reliability and quality standards
- Operate with a platform mindset and product intuition
- AI-native, hands-on with emerging paradigms like agentic coding
- Drive transformation, upskilling teams and embedding new practices
- Business impact (product value, productivity, efficiency)
- Data quality, reliability, and security
- Model serving performance (latency, cost, scalability)
- Platform adoption & speed (AI & agent adoption, time to build, deploy, and evaluate models and agents, budget efficiency)
- ~25+ people in Data (part of 75+ Data & AI org) with squad setup
- Data & Agentic Platform (~17): Data Eng, SRE, MLOps, Agentic Platform
- Data Product & Analytics (~8): Analytics Eng, Data Product
- Data & Agentic Platform
- End-to-end ownership: data → analytics → AI & agents platform
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