Senior Staff Data And Cloud Platform Engineer

Micron Technology; Hyderabad; On-site; INR 1,100,000 - 1,900,000; Full time

Job summary

Micron Technology is a world leader in memory and storage solutions, transforming how information enriches life. We are seeking a Data Platform Engineer to design scalable data ingestion pipelines, orchestrate workflows with Airflow, and implement robust ETL/ELT patterns across multi-cloud environments.

The role involves building schema management, delta detection, and secure infrastructure using Terraform, Kubernetes, and containerized workloads, collaborating with IT, AI, and manufacturing

Qualifications

• Bachelor’s degree in Computer Science or related field.
• 5+ years in data engineering, cloud engineering, or platform engineering.
• Strong Python and SQL with cloud-native data services.
• Experience with Terraform, Kubernetes, Docker, and CI/CD.
• Ability to collaborate with IT, AI, Data Science, Engineering, and manufacturing teams.
• Knowledge of AI-assisted coding techniques is a plus.

Responsibilities

• Design scalable data ingestion pipelines for structured and semi-structured data.
• Develop batch, scheduled, and event-driven workflows using Airflow and cloud services.
• Implement ETL/ELT patterns, data quality checks, and schema management.
• Build schema registries, versioning, evolution, and compatibility controls.
• Develop delta detection and incremental data refresh mechanisms.
• Extract and normalize identifiers like Lot, Die, Wafer, Test, Flow, Product, and Step.
• Set up secure multi-cloud landing zones for validation and analytics pipelines.
• Provision infrastructure using Terraform and CI/CD practices.

Skills

Python

SQL

Automation scripts

Cloud data services

Education

Bachelor's degree in Computer Science or related field

Tools

Terraform

Kubernetes

Docker

CI/CD

GCP

AWS

Job description

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.
• Experience: 5+ years in data engineering, cloud engineering, platform engineering, DevOps, or infrastructure engineering.
• Programming: Strong hands‑on experience with Python, SQL, automation scripts, and cloud‑native data services.
• Cloud Platforms: Practical experience with GCP services such as Dataflow, Pub/Sub, Cloud Functions, BigQuery, Cloud Storage, IAM, and AWS services such as Glue, Lambda, S3, Redshift, IAM.
• Infrastructure & DevOps: Experience with Terraform, Kubernetes, Docker, CI/CD, cloud networking, IAM, and operational automation.
• Collaboration: Ability to partner with IT, AI, Data Science, Engineering, and manufacturing stakeholders to deliver reliable platform capabilities.
• Proven ability to leverage AI‑assisted (vibe) coding techniques to improve efficiency or automate design and analysis methodologies
• Leverage AI tools to automate the tools and workflow
  Applying Artificial Intelligence in workflows to improve build efficiency

Preferred Domain Exposure

• Industrial / Engineering Context: Semiconductor, NAND, product engineering, validation, manufacturing, reliability, quality, test, yield, or RCA data environments.
• AI / Analytics Platforms: Data foundations for AI/ML, analytics, knowledge platforms, digital thread, traceability, or governed data products.
• Certifications: AWS, GCP, Kubernetes, Terraform, or data engineering certifications are preferred but not mandatory.

About Micron Technology, Inc.

We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.

Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.