Senior Manager, Sales Data Architecture & Engineering, Transcatheter Heart Valve

Good decisions are rooted in good data. Founded in Edwards Lifesciences’ strong ethical culture, our commitment to best practice in data governance ensures accuracy, consistency and integrity of data at all levels. This critical function harmonizes data governance policies and processes in collaboration with business units and functions across the globe. As part of this team, you’ll have the resources needed to ensure we are holding ourselves to the highest standards possible.

Together, we ensure responsible data use, allowing Edwards to lead innovative solutions to transform patient lives.

Aortic stenosis impacts millions of people globally, yet it oftenremainsunder-diagnosed and under-treated. Edwards’ groundbreaking work in transcatheter aortic heart valve replacement (TAVR) pioneered an innovative, life-changing solution for patients by offering heart valve replacement without the need for open heart surgery. Our Transcatheter Heart Valve (THV) business unit continues to partner with cardiologists and clinical teams to transform patient care with devices supported by clinical evidence.

It’sour driving force to help patients live longer and healthier lives. Join us and be part of our inspiring journey.

The U.S. Transcatheter Heart Valve (THV) Sales Operations organization is seeking a Senior Manager, Sales Data Architecture & Engineering to lead the data foundation that supports U.S. THV Sales reporting, analytics, planning, operational decision-making, and field-facing business intelligence.

• Lead the design and evolution of U.S. THV Sales data architecture, including SQL database structures, curated datasets, reporting models, and semantic layer design.
• Ensure data is organized in a way that is intuitive, scalable, reliable, and usable for BI developers, analysts, and business users.
• Define reusable business logic, common dimensions, standardized metrics, and analytics-ready datasets for Tableau, Power BI, Excel, and ad hoc reporting.
• Partner with business stakeholders to translate Sales reporting and analytics needs into sustainable data models rather than one-off reporting solutions.
• Support migration of key data assets into Snowflake where appropriate, including requirements, data mapping, validation, and semantic layer readiness.
• Lead the development, maintenance, and continuous improvement of data pipelines that support Sales reporting, dashboards, planning tools, and operational workflows.
• Oversee data ingestion, transformation, validation, testing, documentation, and production support for critical data assets.
• Use advanced SQL expertise to troubleshoot data issues, validate transformations, optimize queries, and support root-cause analysis.
• Establish practical controls for pipeline reliability, exception handling, data refresh monitoring, and issue escalation.
• Partner with IT and technical teams to improve pipeline performance, automation, lineage, and long-term scalability.
• Lead team ownership of U.S. THV Sales SQL databases, including database structure, performance awareness, access needs, refresh dependencies, and operational reliability.
• Monitor and coordinate issue resolution related to database health, server performance, data availability, failed jobs, refresh delays, and other production-impacting issues.
• Partner with IT infrastructure, database administration, and enterprise data teams on server maintenance, environment support, capacity needs, security standards, and platform dependencies.
• Ensure critical sales data environments are documented, supportable, and aligned with business continuity needs.
• Establish operating routines for database maintenance awareness, incident triage, and communication to impacted business users.
• Lead development of a sales-focused semantic layer that allows business users to access consistent, trusted, and understandable data.
• Ensure curated datasets are prepared for multiple consumption paths, including Tableau, Power BI, Excel, SQL-based analysis, and future AI-enabled analytics.
• Reduce duplicated reporting logic by centralizing commonly used calculations, definitions, hierarchies, and business rules.
• Partner with BI developers…