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# Principal Data Engineer

- **Company:** Analog Devices
- **Location:** San Jose, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $267,800.0 - $288,558.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Big Data, Information Systems, Data Architecture, Information Engineering, Data Governance, Data Profiling, Data Structures, Dimensional Modeling, Distributed Computing Environment, Apache Hadoop, Apache Hive, Data Intelligence, Python (Programming Language), Machine Learning, Meta-Data Management, Software Tools, Siebel CRM, PL-SQL, SQL Databases, Systems Integration, Enterprise Data Management, Data Processing, Containerization, Information Technology, Data Lineage, Collibra, Optimization Algorithms, Data Analytics, SAP MDG, Data Management, Machine Learning Operations, Domain Driven Design, Data Pipelines, Amazon Redshift
- **Published:** August 21, 2026
- **Apply:** https://www.oriontalent.com/search-jobs/career/11648939/principal-data-engineer-california-ca-rio-robles

## About the Role

Requirements: Must have a Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related technical field (willing to accept foreign education equivalent) and five (5) years of experience leading large-scale data engineering, governance and MDM initiatives across multiple business domains.

## Description

* Lead enterprise data governance initiatives by leveraging AI-driven data profiling, anomaly detection, and automated classification techniques to establish robust policies, stewardship models, and quality frameworks across critical business domains.
 * Design and implement master data management (MDM) solutions, incorporating machine learning algorithms for entity resolution, taxonomy enrichment, and Customer 360 creation to ensure consistent and intelligent data unification across systems.
 * Build and maintain master data models for supply chain planning using predictive analytics, demand forecasting models, and optimization algorithms to support accurate planning, inventory management, and cross-functional decision-making.
 * Design and develop scalable enterprise data platforms and architectures to support diverse business domains, integrating cloud-native technologies, distributed computing frameworks, and modern data engineering tools for efficient data processing, storage, and analytics.
 * Provide technical leadership and mentorship to engineering teams, fostering a culture of innovation, collaboration, and continuous improvement.
 * Collaborate with business stakeholders, data scientists, and analysts to translate business requirements into scalable technical solutions.
 * Implement data governance practices to ensure data quality, security, and compliance, particularly when handling sensitive or regulated data.
 * Drive innovation by developing new frameworks, prototypes, and automation tools to enhance data engineering capabilities.
 * Stay current with emerging trends and technologies in data engineering and analytics and lead adoption of new tools and methodologies., * Demonstrable experience (DE) implementing Master Data Management (MDM) and data governance frameworks, including stewardship models, metadata management, data quality monitoring, lineage tracking, and regulatory compliance (e.g., GDPR, CCPA) using tools such as Collibra, Informatica IIR/MDM, Siebel UCM, and SAP MDG.
 * DE designing and delivering scalable cloud and on premises data platforms leveraging AWS, Redshift, Hadoop, and Hive, with experience building real time and batch data pipelines using distributed frameworks and big data technologies, and programming in Python, SQL, and PL/SQL.
 * DE applying AI/ML tools/algorithms to solve enterprise data challenges, including entity resolution, predictive modeling, anomaly detection, and intelligent data matching and classification.
 * DE in domain-driven data modeling and advanced analytics, including multi domain data structures, enterprise data architecture, and business-aligned semantic/dimensional modeling.
 * DE leading cross-functional collaboration across business stakeholders, data scientists, engineers, and product teams to translate business requirements into scalable data solutions and analytics capabilities.


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