Staff Engineer, Data Engineering

Analog Devices•Wilmington, MA

1d•$178,547 - $209,715•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 architecting, designing, developing, and maintaining scalable and efficient big data pipelines, ETL processes, and real-time analytics frameworks to process battery data. The Staff Engineer will translate business objectives into functional specifications, identify and implement appropriate data storage and retrieval solutions, and ensure data quality, integrity, and accuracy. Additionally, the role requires deploying and supporting machine learning workflows and AI/ML models in production, collaborating with algorithm engineers, and staying current with emerging trends in data engineering and AI/ML.

Requirements

Master’s degree in Computer Science, Computer Engineering, Software Engineering, Data Engineering, or closely related technical discipline (willing to accept foreign education equivalent) and five (5) years of experience in the offered job or Staff Engineer, Data Engineering-related occupation.
At least (3) years of experience with demonstrated expertise architecting, designing, developing, and maintaining scalable big data pipelines, ETL processes, and real-time analytics frameworks, including experience with cloud-based data infrastructure, AI/ML workflow integration, and data quality processes.
At least (3) years of experience utilizing programming languages such as Python and/or Spark programming to design and build scalable data pipelines for processing large-scale time-series data originating from laboratory or device-based systems.
At least (3) years of experience with analytics engineering and data warehousing, including dimensional and normalized data modeling, schema evolution, and relational and non-relational database design, utilizing tools such as DBT, Snowflake, Redshift, and open table formats including Apache Iceberg or Delta Lake to support scalable cloud-based analytical workloads.
At least (3) years of experience designing and deploying cloud-based data pipelines using storage, compute, data integration, and streaming services such as AWS (S3, EC2, Glue, Lambda, Kinesis).
At least (3) years of experience programming with big data frameworks and data integration tools, including Apache Kafka, Apache Spark, and workflow orchestration platforms (Airflow or Prefect), and proficiency with SQL and NoSQL databases to support batch and large-scale time-series data streaming.
At least (3) years of experience managing and deploying data pipeline infrastructure, including version control systems such as GitHub, CI/CD automation, containerization and orchestration using Docker and Kubernetes, and integrating ML model training and deployment workflows using AWS SageMaker.
At least (3) years of experience collaborating with cross-functional teams including algorithm engineers, hardware or device engineers, and domain experts to gather data requirements, translate business and engineering objectives into functional pipeline specifications, and manage and coordinate deliverables across both internal and external stakeholders.

Responsibilities

Architect, design, develop, and maintain scalable and efficient big data pipelines, ETL (Extract, Transform, Load) processes, and real-time analytics frameworks to process battery data.
Translate business objectives and requirements into functional specifications on data pipelines and manage and coordinate deliverables to both internal and external stakeholders.
Identify and implement appropriate data storage and retrieval solutions based on business needs.
Ensure data quality, integrity, and accuracy through data validation, cleansing, and transformation techniques.
Deploy and support machine learning workflows and AI/ML models in production environments, collaborating with algorithm engineers to integrate model outputs into scalable data pipelines.
Stay up to date with emerging trends and technologies in the field of data engineering and AI/ML, and continuously evaluate and recommend improvements to data infrastructure, tools, and processes.