• Drive Innovation by exploring and implementing the latest AWS and big data technologies. • Collaborate Cross-Functionally with Data Engineers, BI Analysts, and Data Scientists. • Shape Platform Strategy by contributing to the Data Platform vision and roadmap. • Ensure Data Integrity by demonstrating exceptional work ethics and integrity. • Architect Agentic Pipelines for Analytics by designing multi-agent workflows. • Enable AI-Powered Data Products by driving the adoption of LLM-augmented data capabilities.

• Design, implement and operate solutions for HW design data migration within complex semiconductor HW design environments • Set up, configure and validate temporary mitigation or transition systems for HW design data (e.g. EDA data management or configuration management systems) • Develop data migration concepts and strategies for transitions between different HW design data management, configuration management and lifecycle systems (e.g. from legacy platforms to modern PLM/IPLM or version control solutions) • Plan, execute and validate export and import procedures using sample, test and production data • Define and document data topologies, vault / repository structures, data paths, mappings and inventories • Develop, review and validate migration and automation scripts, ensuring data integrity, consistency, and completeness • Support server-side and HW design infrastructure configurations, toolchain and EDA integrations (e.g. Cadence-based environments / DFII) • Collaborate closely with engineering, IT and infrastructure teams and contribute to testing, reviews, documentation and end-user readiness

• Design and maintain enterprise data architecture strategies and scalable data solutions • Lead architecture discussions with technical teams and business stakeholders • Evaluate existing systems and recommend modernization opportunities • Design logical and physical data models to support operational and analytical use cases • Lead data integration, transformation, and interoperability initiatives • Define standards and best practices for data management, governance, and architecture • Support enterprise data lake, warehouse, and reporting initiatives • Develop and maintain architecture documentation, diagrams, and technical standards • Ensure solutions align with security, scalability, and performance requirements • Collaborate with engineering teams on implementation and delivery strategies • Support data lifecycle management and long-term maintainability • Mentor technical teams and provide architecture guidance

• Design, build, and maintain scalable data pipelines and APIs on Google Cloud Platform. • Develop automated workflows and data platforms that support analytics, reporting, and AI/ML use cases. • Implement best practices for data security, governance, CI/CD, and automated deployment. • Collaborate with data engineers, architects, data scientists, and business stakeholders. • Produce high-quality, reusable code and mentor team members on best practices. • Support testing, deployment, monitoring, and production troubleshooting.