• Lead and develop Fusion’s Customer Excellence data practice. • Define and evolve service offerings covering commercial data platforms, CRM ecosystems, and customer master data. • Build and mentor a global team of data and CRM consultants. • Lead customer data and CRM transformation programs across global life sciences organizations. • Support initiatives involving Veeva CRM, Veeva Network, customer master data management, and related commercial platforms. • Ensure solutions align with data governance, compliance, and regulatory requirements. • Define target operating models for customer data and commercial platforms. • Design scalable data governance frameworks and master data lifecycle processes. • Align commercial business needs with technology platforms and data architecture. • Manage and coordinate global project teams across multiple client engagements. • Support capability development and recruitment within the Customer Excellence practice. • Coach consultants and promote best practices across delivery teams. • Contribute to client proposals, solution design, and strategic pitches. • Identify opportunities to expand commercial data and customer excellence programs. • Support the growth of Fusion’s Data & Commercial Platforms portfolio.

• Design, build, and maintain scalable data pipelines and solutions on Microsoft Azure or similar cloud platforms. • Develop and optimize ETL/ELT workflows to support high-volume, high-velocity data ingestion. • Implement robust data models and structures that support analytics, reporting, and machine learning workloads. • Integrate new data sources—internal and external—into the enterprise data ecosystem to expand data availability and unlock new business insights. • Partner with product, engineering, business and global teams to identify opportunities for new datasets and ensure seamless onboarding. • Establish scalable frameworks for data discovery, cataloging, and lineage to support enterprise wide data growth. • Automate data workflows, quality checks, and monitoring using Cloud native tools and Databricks capabilities. • Collaborate with data scientists to operationalize AI models using Databricks, Azure Machine Learning, or similar platforms. • Ensure data readiness, reliability, and accessibility to accelerate AI adoption and experimentation. • Contribute to the development of an enterprise AI strategy by identifying data gaps, opportunities, and scalable patterns. • Work closely with cross functional teams to translate business requirements into scalable data solutions. • Provide technical guidance and best practices on Azure and Databricks to engineering and analytics teams. • Participate in code reviews, architecture discussions, and continuous improvement initiatives. • Assist in identifying & defining new systems functionality within the Go To Market technology stack. • Improve processes/workflows within the Sales organization in regard to Sales applications and system support. • User support - troubleshooting, identifying problems and working with Local & Global IT to resolve technical issues and work with users to provide proper training. • Assist in the analysis of underlying system issues arising from investigations into requirements and problems, and identify available solutions for consideration.

• Own end-to-end data and AI solution architecture across modern cloud data platforms • Design and implement enterprise data architectures across SOO → SOR → CDM (Curated) layers • Lead core Data Architecture disciplines, including: - conceptual, logical, and physical data modeling; MDM and reference data; metadata, lineage, and governance • Design and build scalable batch and real-time data pipelines using lakehouse and warehouse patterns • Enable and support AI / ML use cases on data platforms, including feature engineering, model integration, orchestration, and monitoring • Provide architectural leadership on production-grade systems, covering CI/CD, security, performance, and observability • Partner with engineers, delivery teams, and client stakeholders to align on architectural tradeoffs and delivery decisions • Act as a proactive thought partner, identifying opportunities for simplification, acceleration, and AI-driven value creation

• Design, develop, and maintain scalable data pipelines for batch and near-real-time use cases • Build and optimize Snowflake architecture, including schema design, data modeling, performance tuning, and warehouse optimization • Integrate data from APIs, databases, SaaS platforms, and enterprise systems into a unified data warehouse • Implement and manage ETL/ELT workflows using tools such as dbt, Airflow, or equivalent orchestration frameworks • Ensure data quality, reliability, governance, and proper documentation across the data lifecycle • Develop executive-level dashboards and reports using Tableau and/or Power BI • Translate business requirements into scalable data models and analytics-ready datasets • Analyze complex datasets to identify trends, performance drivers, and operational insights • Optimize dashboard performance, usability, and governance standards • Train business users and promote adoption of BI solutions across the organization • Partner with analysts, AI engineers, delivery managers, and business stakeholders to align data architecture with strategic objectives • Support AI and advanced analytics initiatives by providing clean, curated, and well-structured datasets • Contribute to best practices in data engineering, analytics standards, and platform governance