Manager, Data Analytics - Commercial Analytics

Edwards Lifesciences•Irvine, CA

1d•$121,000 - $171,000•Onsite

About The Position

Aortic stenosis impacts millions of people globally, yet it often remains under-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’s our driving force to help patients live longer and healthier lives. Join us and be part of our inspiring journey. As the Manager, Data Analytics – Commercial Analytics, you will focus on designing and building pipelines and providing guidance into enriching data to meet the needs of our analytics team. Your role will enable data for the team to consume and generate business insights. Working closely with AI engineers, you will also build data pipelines to support AI cases. This role will sit onsite at our Irvine, CA HQ. How you’ll make an impact: Design, build and maintain data pipelines for ingestion, transformation, and delivery across cloud data platforms. Follow standards and best practices for source code control, documentation, and data governance. Implement robust data quality and metadata management processes. Automate workflows for reliability, scalability, and observability. Design, build & maintain curated data products, including models for forecasting, account insights, and executive dashboards. Build and maintain semantic layers and metric definitions to ensure consistency and usability across teams. Publish governed, certified datasets for BI tools; design dashboards and KPI packs that drive decision-making. Enable self-service analytics through reusable data assets and clear documentation. Partner with stakeholders to define metrics and visualization standards. Prepare data for AI use cases (semantic models, metadata enrichment & governance, etc.) Define governance and guardrails for safe AI integration. Collaborate on AI-driven analytics experiences, such as natural language query over governed datasets. Partner with the AI/ML Engineer to provide ML-ready features and pilot/operationalize AI use cases that enhance analyst productivity and business insights. Collaborate and build strong partnerships with stakeholders and cross-functional teams to understand requirements and deliver effective solutions Partner with IT to execute technical solutions, ensuring all work aligns with enterprise IT frameworks, standards, and integration requirements. Bring a self-driven, curious mindset to the team; proactively identify opportunities to improve processes, challenge existing approaches, and drive innovation.

Requirements

Bachelors Degree in related field with 8 years of pervious related work experience or equivalent work experience based on Edwards criteria, or Master’s Degree with 6 years of pervious related work experience or equivalent work experience based on Edwards criteria
Clinical experience (e.g., cardiology, clinics) or equivalent work experience based on Edwards criteria

Nice To Haves

Proven expertise in SQL, Python & data modeling skills
Experience with data lake, data mesh & data as product concepts
Experience with Cloud data platforms (AWS stack, Databricks/ Snowflake), modern transformation tools (dbt, AWS Glue, Fivetran, etc.) & source code management (Git)
Experience with BI & visualization tools (Tableau, PowerBI, Thoughspot)
Excellent written and verbal communication skills and interpersonal relationship skills including consultative and relationship management skills
Good understanding of sales & commercial workflows
Strict attention to detail
Ability to interact professionally with all organizational levels and proactively escalate issues to appropriate levels of management in the organization
Ability to manage competing priorities in a fast-paced environment
Interacts with team members and other managers and may include vendors
Adhere to all company rules and requirements (e.g., pandemic protocols, Environmental Health & Safety rules) and take adequate control measures in preventing injuries to themselves and others as well as to the protection of environment and prevention of pollution under their span of influence/control

Responsibilities

Design, build and maintain data pipelines for ingestion, transformation, and delivery across cloud data platforms.
Follow standards and best practices for source code control, documentation, and data governance.
Implement robust data quality and metadata management processes.
Automate workflows for reliability, scalability, and observability.
Design, build & maintain curated data products, including models for forecasting, account insights, and executive dashboards.
Build and maintain semantic layers and metric definitions to ensure consistency and usability across teams.
Publish governed, certified datasets for BI tools; design dashboards and KPI packs that drive decision-making.
Enable self-service analytics through reusable data assets and clear documentation.
Partner with stakeholders to define metrics and visualization standards.
Prepare data for AI use cases (semantic models, metadata enrichment & governance, etc.)
Define governance and guardrails for safe AI integration.
Collaborate on AI-driven analytics experiences, such as natural language query over governed datasets.
Partner with the AI/ML Engineer to provide ML-ready features and pilot/operationalize AI use cases that enhance analyst productivity and business insights.
Collaborate and build strong partnerships with stakeholders and cross-functional teams to understand requirements and deliver effective solutions
Partner with IT to execute technical solutions, ensuring all work aligns with enterprise IT frameworks, standards, and integration requirements.
Bring a self-driven, curious mindset to the team; proactively identify opportunities to improve processes, challenge existing approaches, and drive innovation.