Principal Data Engineer

Analog Devices•San Jose, CA

2d•$267,800 - $288,558•Hybrid

About The Position

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X. This role involves leading enterprise data governance initiatives, designing and implementing master data management (MDM) solutions, building and maintaining master data models for supply chain planning, and designing and developing scalable enterprise data platforms and architectures. The Principal Data Engineer will also provide technical leadership and mentorship, collaborate with stakeholders, implement data governance practices, and drive innovation by developing new frameworks and automation tools. Staying current with emerging trends and technologies in data engineering and analytics is also a key aspect of this position.

Requirements

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

Responsibilities

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.