Job Specifications
Sr. Director Enterprise Data and AI Platform
Our miltinational client is seeking an experienced and visionary Sr. Director of Enterprise Data and AI Platform to lead the strategic direction, design, and execution of our global enterprise data and AI capabilities to support our business functions such as Sales, Marketing, Finance, HR, Customer success, support and services, legal etc. This role will be responsible for architecting and maintaining our modern data estate, enabling advanced analytics and machine learning on Azure Data Lake, driving data needs for enterprise reporting, and leading the strategy, platform selection, and implementation of Agentic AI capabilities.
The successful candidate will manage a diverse global teams, including direct reports, managers, and external partnersaEUR"and oversee platform architecture, data engineering, and vendor relationships. This role is both highly strategic and hands-on, demanding deep technical expertise, cross-functional leadership, and a passion for data-driven innovation.
This role will report to VP, Enterprise Data, analytics and governance.
Must-Have Skills & Weightage
Sr. Director, Enterprise Data & AI Platform (Data, AI, People leadership and strong hands on technical knowledge)
Essential Skills & Requirements:
* Must have an advanced degree in computer science, data science, machine learning, or a related field is a plus.
* 10+ years of experience in Enterprise data and AI leadership roles.
3. Enterprise Data Strategy & Leadership (Director+ for 3+ years)
Proven track record leading enterprise-wide data/AI strategy, aligning with business goals, and managing senior stakeholders. Good to have B2B SaaS industry experience.
4. People Leadership & Global Team Building
Demonstrated success in hiring, developing, and retaining talent across global teams (onshore/offshore). Includes building culture, succession planning, and scaling org structures.
5. Cloud Data Platforms (Azure, AWS, Snowflake, Databricks, etc.)
Deep expertise in modern cloud-native data architectures (lakes, warehouses, lakehouses, data mesh, streaming).
6. Generative AI & Agentic AI Enablement
Hands-on understanding of GenAI (LLMs, RAG, embeddings, fine-tuning) and Agentic AI frameworks, with experience operationalizing real-world use cases. Ability to roll up their sleeves and explore tech.
7. AI/ML Platform & MLOps
Experience enabling machine learning at scale, deployment, monitoring, model governance, and integration into enterprise data products.
8. Data Governance, Security & Compliance
Expertise in building governance frameworks, lineage, metadata, data stewardship, and regulatory compliance (HIPAA, GDPR, SOX, etc.).
9. Data Engineering & Modeling
Strong understanding of data pipelines, ETL/ELT, schema design, and scalable modeling frameworks.
10. Scalability & Performance Engineering
Ability to design platforms for high-volume ingestion, distributed compute, and cost-optimized performance at scale.
11. Budget, Vendor & Partner Management
Experience with budget ownership, vendor selection, contract negotiations, and managing partner ecosystems.
Rationale
Leadership - Balanced across enterprise strategy and people/global team leadership. Evaluate not just for technical vision, but also for their ability to scale organizations worldwide.
Platform & AI Expertise - Cloud data platforms, GenAI/Agentic AI, and ML/MLOps are at the heart of enterprise transformation.
Governance & Engineering - Ensures robustness, compliance, and sustainable architecture.
Execution & Operations - Budget and vendor management are important, but secondary compared to strategy and people leadership.
Desired Experience:
* Expertise in modern data stack and infrastructure: Azure Cloud, Data Lake/Warehouse/Mesh, Data Fabric, data integration tools, Power BI, Tableau.
* Proven leadership of Data & ML Engineering/Analytics in global environment.
* Deep understanding of ML operations (MLOps) practices, including model monitoring, retraining, and lifecycle management.