Sr Cloud Data Engineer, SMAI

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.

As a Sr. Data Engineer at Micron Technology Inc., you will be a key member of a multi-functional team responsible for developing and scaling Micron’s data platforms and AI‑ready data foundations in cloud. You will collaborate closely with data scientists, Architects, AI engineers, product teams, and business stakeholders to enable advanced analytics, machine learning, and Generative AI use cases across manufacturing, supply chain, and business systems.

You will design, build, and operate high‑quality, governed, and scalable data pipelines that power AI models, GenAI applications, and enterprise insights.

Responsibilities And Tasks Understand the Business Problem and the Relevant Data and AI Use cases

• Maintain a deep understanding of business strategy, operational workflows, and AI/ML use cases
• Translate business, analytics, and AI/ML requirements into scalable data architecture and pipelines
• Identify and evaluate structured, semi‑structured, and unstructured data sources relevant to predictive analytics, optimization, and GenAI
• Develop conceptual and logical data models optimized for feature engineering and model consumption
• Define data quality, lineage, and observability objectives required for reliable AI and ML outcomes
• Act as a domain expert for data availability, AI readiness, and reporting options

Architect AI-Ready Data Management Systems

• Select and design data platforms (Big Data, OLTP, OLAP, Lakehouse) that support analytics, ML training, and inference workloads
• Design and implement optimized data structures for model training, feature stores, and real‑time inference
• Enable scalable storage and processing platforms (e.g., Google Cloud Platform(GCP), Snowflake, Azure, cloud data platforms, SQL engines) for AI workloads
• Design data retention, archiving, and lifecycle strategies aligned to AI model governance and compliance

Develop, Automate, and Orchestrate an Ecosystem of ETL Processes for AI & Analytics

• Build and orchestrate batch, streaming, and near‑real‑time pipelines to support AI/ML and GenAI applications
• Implement robust transformation pipelines to support feature engineering, embeddings, and model inputs
• Enable ingestion and processing of unstructured data (logs, text, sensor data) for LLMs and sophisticated analytics
• Optimize pipelines for performance, scalability, and cost across large‑volume AI datasets
• Implement automation, monitoring, and alerting to ensure pipeline reliability for AI workloads

Prepare and Serve Data for AI and Advanced Analytics

• Partner with data scientists and ML engineers to enable:

• Feature creation and reuse
• Training, validation, and inference datasets

• Feedback loops for model performance

• Develop reusable, governed data assets for AI experimentation and production
• Implement standards for data versioning, reproducibility, and audit-ability.
• Ensure datasets meet quality and consistency standards required for enterprise-grade AI solutions

Enable Generative AI (GenAI) & Sophisticated AI Use Cases

• Build and lead data workflows that support Generative AI use cases, including:

• Large Language Models (LLMs)
• Vector databases and embedding pipelines

• Retrieval-Augmented Generation (RAG) architectures

• Enable secure and compliant access to enterprise data for GenAI applications
• Support timely development and model grounding by ensuring accurate, governed data retrieval
• Collaborate with AI platform and MLOps teams to operationalize AI and GenAI solutions at scale

Governance, Security, and Responsible AI

• Ensure data pipelines align with enterprise security, privacy, and governance standards
• Implement lineage, metadata management, and access controls to support Responsible AI
• Partner with platform and compliance teams to ensure AI data usage aligns with corporate policies

Continuous Improvement and Innovation

• Evaluate and adopt emerging AI, GenAI, and data engineering technologies
• Drive standardization and standards for AI-ready data platforms
• Mentor engineers on AI data patterns, effective processes, and scalable builds
• Chip in to AI roadmap discuss…