Staff Data Engineer

We are seeking a Data Engineer with deep experience in managing and processing high-volume IoT data to enable the cloud-based training of machine learning models that power real-time inference on edge devices. In this role, you will architect and maintain cloud-based data infrastructure and pipelines that support ML workflows for training, validation, and deployment of machine learning models optimized for deployment in edge environments. This is a multi-functional role requiring close collaboration with ML engineers, systems engineers, cloud architects, and embedded systems teams to deliver high-quality, efficient, and scalable data solutions that power intelligent behavior on resource-constrained devices such as fixed and handheld RFID readers.

• Design data workflows to support model training, evaluation, and retraining cycles for deployment on edge devices
• Work closely with ML engineers to align data formats, labeling standards, feature extraction for edge-compatible models, and feedback loops for model improvement
• Architect and maintain scalable data pipelines to ingest, process, store, and access large volumes of structured and semi-structured RFID time-series data from edge networks
• Develop automated systems for data versioning, labeling, augmentation, and quality assurance
• Establish and maintain data APIs and interfaces to query, consume, and update datasets
• Manage large datasets using distributed storage and compute frameworks (e.g., Apache Spark, Hadoop, or Dask)
• Ensure data security, compliance, and consistency across the full data lifecycle
• Drive improvements in data performance and reliability, especially for low-latency ML inference use cases 
• Implement robust ETL/ELT workflows for preparing data for cloud-based ML model training and evaluation
• Collaborate and coordinate with large scale data collection projects
• Monitor and optimize data pipelines for performance, reliability, and cost across edge-to-cloud infrastructure
• Optimize data flow and compute for performance, cost, and latency in hybrid edge-cloud environments

• Bachelor’s degree in Data Engineering, Electrical Engineering or a related field and 8 years of related experience, or equivalent combination of education and experience
• 8+ years of experience in data engineering working with Machine Learning pipelines
• Deep understanding of data pipeline design, ETL/ELT processes, automated workflow orchestration (e.g. Apache Airflow)
• Strong programming skills in Python (especially for data workflows), with experience building scalable, maintainable pipelines. (e.g. Pandas, numpy)
• Strong experience with structured and unstructured databases (SQL, MongoDB, DuckDB)
• Strong understanding of cloud infrastructure (AWS, Azure, or GCP), especially cloud storage, compute, and ML tools (e.g., SageMaker, Vertex AI, Azure ML)
• Experience with data lake/data warehouse technologies (e.g., S3 + Glue, BigQuery, Snowflake, Delta Lake)
• Knowledge of machine learning model lifecycles, including training, validation, and deployment
• Understanding of data versioning, feature engineering, and ML lifecycle management
• Understanding of machine learning data needs, including labeling, versioning, and model-ready dataset preparation
• Familiar with distributed data systems and big data tools (e.g., Spark, Kafka, Hadoop)

The benefits listed below may vary depending on the nature of your employment with Impinj and the country where you work.

The typical base pay range for this role across the US is $129,000 - $200,000. Individual base pay depends on various factors such as complexity and responsibility of role, job duties, requirements, and relevant experience and skills. Both market wage data and the mid-point of the pay range is reviewed and used as the starting point for all new hire offers. Offers are made within the base pay range applicable at the time.
At Impinj certain roles are eligible for additional rewards, including merit increases, annual bonus and stock. These awards are allocated based on individual performance. In addition, certain roles also have the opportunity to earn sales incentives based on revenue or utilization, depending on the terms of the plan and the employee’s role. US based employees have access to healthcare benefits; a 401(k) plan and company match among others.

For a more comprehensive list of US employment benefits, click here. 

This position has access to technologies or data subject to U.S. export control regulations. Under these laws, the release or transfer of export-controlled items or information to individuals who are not classified as "U.S. persons" (as defined by Immigration & Nationality Act) may require prior authorization from the U.S. government. We may require additional documentation related to national identity to determine whether an export compliance license is required for any export-controlled items. This information is requested solely for the purpose of complying with U.S. export control laws and will not be used for other purposes. Learn more about export compliance here.