About the role

AI summarised

Senior Data Architect role at Applied Materials, a semiconductor equipment leader. Responsible for designing and building scalable data pipelines to support AI/ML models in global supply chain and planning operations. Requires 12-14 years experience with SQL, Python, Spark, and Azure cloud platform.

EquipmentFull-time

Key Responsibilities

Design, build, and maintain scalable, reliable data pipelines to support AI/ML models and analytics in global supply chain and planning operations.
Collaborate with data scientists and AI engineers to prepare and transform structured and unstructured data for modeling and simulation.
Develop and optimize ETL/ELT processes using tools like Databricks, Spark.
Ensure data quality, versioning, and lineage across the ML lifecycle.
Support the deployment and monitoring of AI models in production environments.
Translate business requirements into technical solutions, working closely with cross-functional teams.
Contribute to the development of data services and APIs that power intelligent decision-making tools.

Requirements

12-14 Years of strong experience in Data Architect roles
Strong expertise in SQL, Python, and distributed data processing (e.g., Spark, PySpark).
Experience with cloud-based data platforms (Azure).
Familiarity with MLOps tools and practices (e.g., MLflow) is a strong advantage.
Experience working with AI/ML frameworks (e.g., TensorFlow, PyTorch) or supporting model training and deployment is a strong advantage.
Understands the role of data in driving supply chain and planning decisions.
Able to align data engineering solutions with business KPIs and operational goals.
May lead technical workstreams or mentor other engineers.
Takes ownership of data infrastructure components and contributes to architectural planning.
Solves complex data challenges with creativity and precision.
Proactively identifies and resolves data quality, performance, and scalability issues.
Enables the success of AI/ML initiatives by ensuring robust, scalable, and high-quality data infrastructure.
Supports real-time and predictive decision-making across global operations.
Communicates effectively with data scientists, AI engineers, and business stakeholders.
Comfortable working in a fast-paced, collaborative environment with evolving priorities.