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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Engineer, Data Engineering - **Company:** Analog Devices - **Location:** Wilmington, MA, United States (Remote available) - **Experience:** Experienced - **Salary:** $178,547.0 - $209,715.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Apache HTTP Server, Big Data, Cloud Database, Computer Engineering, Data Validation, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Warehousing, Github, Python (Programming Language), Machine Learning, NoSQL, Software Engineering, SQL Databases, Systems Integration, Workflow Management Systems, Delivery Pipeline, Snowflake, Apache Spark, Containerization, Data Lakes, Information Technology, Enterprise Integration, Apache Kafka, Non-relational Database, Machine Learning Operations, Stream Analytics, Software Version Control, Data Pipelines, Docker - **Published:** September 22, 2026 - **Apply:** https://www.oriontalent.com/search-jobs/career/11663416/staff-engineer-data-engineering-massachusetts-ma-wilmington ## About the Role Requirements: Must have a 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. Position also requires at least (3) years of experience with each of the following: * Demonstrated Expertise (DE) 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. * DE 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. * DE 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. * DE designing and deploying cloud-based data pipelines using storage, compute, data integration, and streaming services such as AWS (S3, EC2, Glue, Lambda, Kinesis). * DE 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. * DE 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. * DE 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. ## Description * 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. ## Related Videos - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [NoSQL Data Modeling for Front-end Developers](https://www.wearedevelopers.com/videos/297-nosql-data-modeling-for-front-end-developers) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Top-Paying Tech Jobs (with Salaries)](https://www.wearedevelopers.com/magazine/372-top-paying-tech-jobs-with-salaries) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Is Software Engineering Over-Saturated?](https://www.wearedevelopers.com/magazine/418-is-software-engineering-over-saturated)