Senior Principal Data Architect

Marvell Technology•Santa Clara, CA

About The Position

About Marvell Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities. At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead. Your Team, Your Impact Marvell Data Analytics Team is at the forefront of driving Marvell’s Data Intelligence, Strategy, and Architecture initiatives. As a high-visibility team, we deliver critical data insights and analytics solutions to the operations and leadership group that empower data-driven decision-making at the highest levels. Our team is responsible for executing global, high-impact projects that add significant value across the organization. By leveraging cutting-edge data analytics technologies and methodologies, we ensure that Marvell remains a leader in harnessing data for business advantage. As Marvell continues to expand its data capabilities, the complexity and scale of our data environments have increased. This Data Architect will ensure that our data infrastructure is robust, scalable, and aligned with the organization’s strategic goals. This role will lead the design and management of data architectures that support our current and future business needs. What You Can Expect Data Architecture & Modeling: Design and implement scalable, efficient, and secure data architectures that support data engineering and data science projects Develop and maintain conceptual, logical, and physical data models for structured and unstructured data Ensure that data models and architecture align with business requirements and industry best practices Cloud Platforms & Data Engineering: Design and manage data solutions on AWS, utilizing services such as S3, EMR, and other relevant AWS data services Develop and maintain data pipelines using Python and Apache Airflow for ETL/ELT processes Ensure data workflows are optimized for performance and scalability Optimize data storage and retrieval processes to support analytics and reporting needs Data Security & Governance: Implement and enforce data governance policies and procedures to ensure data accuracy, integrity, and security Collaboration & Stakeholder Management: Collaborate with data engineers, data scientists, and business intelligence teams to design and implement data solutions that meet business needs Work with business stakeholders to understand data requirements and translate them into technical specifications Provide guidance and mentorship to junior team members in data architecture and engineering best practices Business Intelligence & Reporting: Collaborate with the BI team to ensure that data models support reporting and analytics needs, particularly in tools like Power BI Ensure that data is available, accurate, and accessible to end-users for reporting and decision-making AI and Gen AI Expertise: Collaborate with data science and AI teams to design data architectures that support AI and Gen AI initiatives Stay informed about the latest advancements in AI and Gen AI technologies and explore opportunities to integrate these into the organization’s data strategy Contribute to the continuous improvement of AI-related data processes and infrastructure, ensuring scalability and efficiency Technical Expertise & Continuous Improvement: Stay up-to-date with emerging technologies and trends in data architecture, engineering, analytics, AI, and Gen AI Continuously evaluate and improve existing data architectures to enhance performance, scalability, and security Drive innovation by exploring and recommending new tools and technologies to improve the organization’s data capabilities

Requirements

Bachelor’s degree in Information Systems, Computer Science, or a related field (or equivalent education/experience)
8+ years of experience architecting and developing end‑to‑end data and analytics solutions
Extensive experience in data modeling and data warehousing
Experience with Snowflake and cloud platforms. Expertise preferred. AWS highly preferred.
Proficiency in Python preferred
Strong skills in PL/SQL
Ability to design, develop, and manage scalable data architectures
Experience supporting data engineering and data science projects
Understanding of data security best practices. Expertise preferred.
Hands‑on experience with Airflow for workflow orchestration preferred
Experience in data governance, data quality, and metadata management preferred
Ability to collaborate closely with data engineers, data scientists, and BI teams
Strong communication skills and ability to translate business needs into data solutions

Responsibilities

Design and implement scalable, efficient, and secure data architectures that support data engineering and data science projects
Develop and maintain conceptual, logical, and physical data models for structured and unstructured data
Ensure that data models and architecture align with business requirements and industry best practices
Design and manage data solutions on AWS, utilizing services such as S3, EMR, and other relevant AWS data services
Develop and maintain data pipelines using Python and Apache Airflow for ETL/ELT processes
Ensure data workflows are optimized for performance and scalability
Optimize data storage and retrieval processes to support analytics and reporting needs
Implement and enforce data governance policies and procedures to ensure data accuracy, integrity, and security
Collaborate with data engineers, data scientists, and business intelligence teams to design and implement data solutions that meet business needs
Work with business stakeholders to understand data requirements and translate them into technical specifications
Provide guidance and mentorship to junior team members in data architecture and engineering best practices
Collaborate with the BI team to ensure that data models support reporting and analytics needs, particularly in tools like Power BI
Ensure that data is available, accurate, and accessible to end-users for reporting and decision-making
Collaborate with data science and AI teams to design data architectures that support AI and Gen AI initiatives
Stay informed about the latest advancements in AI and Gen AI technologies and explore opportunities to integrate these into the organization’s data strategy
Contribute to the continuous improvement of AI-related data processes and infrastructure, ensuring scalability and efficiency
Stay up-to-date with emerging technologies and trends in data architecture, engineering, analytics, AI, and Gen AI
Continuously evaluate and improve existing data architectures to enhance performance, scalability, and security
Drive innovation by exploring and recommending new tools and technologies to improve the organization’s data capabilities