We are seeking a visionary, high-impact Enterprise Data Architecture Leader to drive our client's global data modernization and transformation strategy. In this role, you will bridge the gap between business strategy and technical execution, defining the next-generation architecture for our data ecosystem. You will spearhead the transition to a decentralized Cloud Data Mesh , establish robust governance, and lead the integration of legacy enterprise systems (including SAP) with modern cloud-native platforms.

If you are a seasoned leader with a proven track record of scaling data platforms, fostering innovation, and mentoring world-class engineering teams, we want you on the team.

1. Define & Evolve Strategy: Lead the definition and evolution of the enterprise data architecture strategy, frameworks, standards, and operating models aligned with overarching business and IT goals.
2. Data Asset Management: Champion the ownership, implementation, and scaling of the enterprise data catalog, data lineage, and metadata management solutions.
3. Domain & MDM Excellence: Act as the Lead Domain Architect across key business areas; design business-aligned data models, semantic layers, and standardized KPIs while driving Master Data Management (MDM) adoption.

1. Scalable Infrastructure: Architect resilient, scalable data platforms and high-throughput ingestion pipelines (both batch and streaming) utilizing Kafka, Informatica, Databricks, APIs , and cloud-native services.
2. SLA & Performance Optimization: Ensure all platforms meet rigorous enterprise SLAs for system availability, data quality, and processing performance.

1. Robust Compliance: Establish and enforce comprehensive data governance, security, and privacy standards across the global enterprise.
2. Modern Engineering Practices: Standardize DataOps and DevOps methodologies by implementing continuous integration/continuous delivery (CI/CD), Infrastructure as Code (IaC/Terraform), and automated data quality testing.

1. Cloud FinOps Leadership: Drive cloud financial accountability by designing cost-efficient data architectures, monitoring usage patterns, and optimizing resource consumption through serverless and reserved capacity models.

1. Advanced Analytics & AI/ML: Enable next-generation AI/ML use cases and advanced analytics capabilities by leading innovation pilots and Proofs of Concept (PoCs).
2. Strategic Partnership: Provide architectural oversight and partner closely with executive business and technology stakeholders to drive data maturity and a data-driven culture.

1. Experience: 15+ years of progressive experience in enterprise data architecture, domain modeling, and large-scale data engineering.
2. Architectural Paradigms: Proven track record of designing and executing decentralized Cloud Data Mesh architectures, preferably on AWS .
3. Governance Expertise: Deep hands-on experience implementing governance, security, and master data frameworks using Databricks Unity Catalog, AWS IAM , and Informatica MDM .
4. Full Lifecycle Mastery: Comprehensive expertise across the entire data lifecycle—from complex ingestion (SAP tools, low-code/no-code) and transformation (Spark ) to consumption via APIs and advanced analytics platforms.
5. Tech Stack Proficiency: Strong expertise in Kafka, Informatica, and Databricks. Experience with Palantir is highly preferred.
6. Automation & Cloud Native: Solid understanding of cloud-native DevOps, automated pipelines, and Infrastructure as Code (Terraform ).
7. Leadership & Influence: Exceptional stakeholder management, communication, and storytelling skills, with a demonstrated ability to mentor technical teams and influence C-suite decisions.
8. Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field.

1. Impact: The opportunity to lead high-visibility, enterprise-wide data transformation initiatives that modernize and optimize our global data footprint.
2. Influence: A culture that empowers you to drive data-driven decision-making and shape strategic business and IT initiatives at the highest levels.
3. Innovation: A well-funded environment to explore and implement emerging technologies, advanced analytics solutions, and AI/ML frameworks.
4. Global Scale: Collaboration with a diverse, talented global team dedicated to building high-performance, industry-leading data platforms.
5. Growth: A commitment to fostering a culture of continuous improvement, data excellence, and professional mentorship.