Enterprise Data Transformation Architect - USA Remote

At Cepheid, we are passionate about improving health care through fast, accurate diagnostic testing. Our mission drives us, every moment of every day, as we develop scalable, groundbreaking solutions to solve the world’s most complex health challenges. Our associates are involved in every stage of molecular diagnostics, from ideation to development and delivery of testing advancements that improve patient outcomes across a range of settings. As a member of our team, you can make an immediate, measurable impact on a global scale, within an environment that fosters career growth and development.

Cepheid is proud to work alongside a community of six fellow Danaher Diagnostics companies. Together, we’re working at the pace of change on diagnostic tools that address the world’s biggest health challenges, driven by knowing that behind every test there is a patient waiting.

Learn about the Danaher Business System which makes everything possible.

The Enterprise Data Transformation Architect is responsible for leading the design, development, and deployment of data transformation architecture across the organization. This role will play a key part in shaping Cepheid’s data strategy, ensuring that data systems are robust, scalable, and aligned with business goals. You will collaborate with cross-functional teams, including data engineering, data science, IT, and business stakeholders, to drive data transformation architecture from conception to execution.

This position is part of the AI and Enterprise Data Transformation and will be in remote. At Cepheid, our vision is to be the leading provider of seamlessly connected diagnostic solutions.

In this role, you will have the opportunity to:

• Translate Cepheid’ business requirements into technical specifications, including data streams, integrations, transformations, databases, and data warehouses.

• Define the data architecture framework, standards, and principles, including modeling, metadata, security, reference data such as product codes and object categories, and master data.

• Define data flows, i.e., which parts of the organization generate data, which require data to function, how data flows are managed, and how data changes in transition.

• Establish and enforce data governance policies by collaborating with Data Management teams to safeguard the quality, accuracy, and security of data used in data automation processes while ensuring the data infrastructure supports AI/ML initiatives.

• Define and track key performance indicators (KPIs) to measure the efficacy/impact of Data Transformation and automation initiatives while presenting on the progress and outcomes of key projects to executive leadership.

The essential requirements of the job include.

• Bachelor’s degree in computer science, data engineering, or …