Senior /Staff Data & Cloud Platform Engineer

Job description

Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Responsibilities  • Data Pipeline Engineering: Build scalable ingestion pipelines for structured, semi-structured, and engineering data sources.  • Workflow Orchestration: Develop reliable batch, scheduled, and event-driven workflows using Apache Airflow and cloud-native services.  • ETL / ELT Frameworks: Implement reusable pipeline patterns, transformations, incremental ingestion, and data quality checks.  • Schema Management: Implement schema registry, schema versioning, schema evolution, and compatibility controls.  • Delta Detection: Build change detection and incremental refresh mechanisms for efficient large-scale data synchronization.  • Entity Extraction: Extract and normalize key identifiers such as Lot, Die, Wafer, Test, Flow, Product, and Step.  • Cloud Landing Zones: Set up secure AWS, GCP, and on-prem landing targets for validation and analytics pipelines.  • Infrastructure Automation: Provision infrastructure using Terraform, CI/CD, and Infrastructure-as-Code practices.  • Platform Operations: Support containerized workloads using Kubernetes, Docker, monitoring, logging, and operational controls.  • Security & Cost Optimization: Implement IAM, RBAC, hybrid connectivity, access controls, and cost optimization for TB-scale data.  • Cross-Functional Delivery: Partner with IT, TPG AI, SMAI, Data Science, Product Engineering, and platform teams.  Expertise  • Data Pipeline Engineering: Design and operation of scalable ingestion pipelines, ETL/ELT frameworks, metadata-driven processing, and data quality validation.  • Workflow Orchestration: Development and management of batch, event-driven, and scheduled workflows using Apache Airflow.  • Cloud Data Platforms: Building data solutions using GCP and AWS services for ingestion, processing, storage, and analytics.  • Schema & Metadata Management: Implementation of schema registries, schema evolution, version control, metadata capture, and lineage readiness.  • Incremental Data Processing: Development of delta detection, CDC-style processing, watermarking, and efficient refresh strategies.  • Manufacturing Data Modeling: Extraction and normalization of Lot, Die, Wafer, Test, Flow, Product, Step, and validation identifiers.  • Multi-Cloud Architecture: Secure and scalable data platforms across AWS, GCP, and on-premises environments.  • Infrastructure Automation: Cloud provisioning using Terraform, automation frameworks, reusable modules, and CI/CD practices.  • Container & Platform Engineering: Operation of containerized applications and platform workloads using Kubernetes and Docker.  • Cloud Security & Governance: IAM, RBAC, data protection, service accounts, secrets management, and enterprise access controls.  • Hybrid Cloud Connectivity: Networking, VPN, private connectivity, and secure integration between cloud and on-prem environments.  • Reliability & Cost Management: Monitoring, observability, performance tuning, troubleshooting, and optimization for TB-scale workloads.  Qualifications  • Education: Bachelor's degree in Computer Science, Data Engineering, Cloud Engineering, Information Systems, or related technical field.</spa