Principal Engineer, Data Engineering

Analog Devices; Wilmington (DE); Hybrid; USD 217,000 - 251,000; Full time

Benefits offered by this job

Partial telecommute (2 days/week)

Job summary

Analog Devices, Inc. is seeking a Principal Engineer, Data Engineering to own end-to-end data platforms enabling AI and analytics across products. You will lead architecture, standards and governance, mentoring teams and partnering with AI/ML and software engineers.

This role supports hybrid work with partial telecommuting. Required master’s degree with extensive experience in distributed systems, ETL, cloud data platforms, and data governance.

Qualifications

• Master's degree or foreign equivalent accepted; 5+ years in data products lifecycle preferred.
• Proven architecture, data product ownership and lifecycle management.
• Experience with distributed and streaming data systems at scale.

Responsibilities

• Design end-to-end data architecture for scalable AI/analytics systems.
• Define data models, ingestion, storage and transformation standards.
• Lead data governance, quality, observability, and security across domains.
• Mentor data engineers; set coding and design review standards.
• Collaborate with AI/ML teams to train models on large datasets.
• Translate business problems into scalable data solutions and roadmaps.

Skills

Distributed systems

Streaming systems

Data engineering tools

Spark

Kubernetes

Terraform

Kafka

Airflow

Hadoop

ETL tools

SQL

Python

BigQuery

Dataflow

Synapse

Redshift

Tableau

Looker

Grafana

Datadog

Education

Master's degree in IT/CS/EE/CE or related field

7+ years of experience OR 5+ years with Master’s

Tools

Spark

Kubernetes

Terraform

Kafka

Pub/Sub

Airflow

Informatica

Talend

SQL

Python

Oracle

SQL Server

Vertica

Looker

Tableau

Spark (again)

Job description

About Analog Devices

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.

Employer: Analog Devices, Inc.

Job Title: Principal Engineer, Data Engineering

Job Requisition: 1010.852.3 / R265694

Job Location: Wilmington, Massachusetts

Job Type: Full Time

Rate of Pay: $216,643 - $250,920 per year

Duties

• Develop strategies, design and own end-to-end data architecture to build scalable and resilient systems across diverse applications to enable modern AI and analytics solutions.
• Define and champion engineering best practices, frameworks and standards for data modeling, ingestion, storage and transformation for large-scale processing of data.
• Drive the frameworks to implement Data Quality, Observability and Data Governance across domains.
• Provide technical leadership to data teams and mentor data engineers by setting coding standards and best practices with thorough code reviews and design reviews.
• Collaborate with AI/ML and software engineers to train and tune AI models and enable fast experimentation and deployment with large datasets.
• Lead the execution of large scale, complex data and analytics efforts, as well as mentor complex projects using a wide breadth of data science and analytical techniques.
• Serve as a subject matter expert (SME) in key strategic data domains within the organization.
• Collaborate with business stakeholders to identify business problems and translate them into requirements and scalable technical designs.
• Lead strategic and innovative solutions by advising and influencing business leaders and senior stakeholders with data-driven insights.
• Communicate the value of data clearly by articulating both the high-level concept and detailed user stories for feature enhancements and convince teams to adopt data-driven improvements.
• Drive innovation by creating new frameworks, standards, prototypes, and automation projects in relevant areas.
• Influence the design decisions for projects across teams to capture analytical and AI/ML use cases.

Partial Telecommute Benefit (2 days/week work from home)

Requirements

Must have a Master's degree in Information Technology, Computer Science, Electrical Engineering, Computer Engineering, or a related field (willing to accept a foreign education equivalent) and five (5) years of experience in the proffered role or related occupation defining, architecting, and rolling out data products, including ownership of data products through their entire lifecycle. Or in the alternative, a Bachelor's degree in Information Technology, Computer Science, Electrical Engineering, Computer Engineering, or a related field (willing to accept a foreign education equivalent) and seven (7) years of experience in the proffered role or related occupation defining, architecting, and rolling out data products, including ownership of data products through their entire lifecycle.

Must also possess the following (quantitative experience requirements not applicable to this section):

• Demonstrated Expertise ("DE") with distributed systems, streaming systems, and data engineering tools, such as Spark, Kubernetes, Terraform, Kafka, Pub Sub, Airflow, or Hadoop.
• DE with ETL tools such as Informatica, Talend, SQL, or Python, databases like Oracle, SQL Server or Vertica), analytical and visualization tools Looker, AtScale, Tableau, or Business Objects.
• DE with cloud-native data architectures and services/platforms, such as GCP (BigQuery, Dataflow) or Azure (Synapse, Fabric) or AWS (Redshift, Glue), with a focus on scalable, cost-effective solutions.
• DE in data governance, security, and compliance, including implementation of data quality frameworks, lineage tracking, and access controls across distributed data environments.
• DE in applying foundational machine learning concepts to data engineering workflows, enabling model deployment, and forecasting techniques to empower non-technical stakeholders to access insights and automate decision-making.
• DE in observability, monitoring, and performance tuning for distributed data systems, and building observability frameworks using tools like Grafana, Datadog and custom metric instrumentation.
• DE with architect and implement large scale complex projects and lead and mentor data engineers.
• DE