Senior Data Engineer

Atomic Machines is ushering in a new era of micromanufacturing with its Matter Compiler™ technology platform. This platform enables new classes of micromachines to be designed and built by providing manufacturing processes and a materials library that are inaccessible to semiconductor manufacturing methods. It unlocks MEMS manufacturing not only for device classes that could never be produced by semiconductor methods, but also for entirely new categories. Furthermore, this digital platform is fully programmable in the way 3D printing is digital—but whereas 3D printing produces parts of a single material using a single process, the Matter Compiler™ technology platform is a multi-process, multi-material system: bits and raw materials go in, and complete, functional micromachines come out. The Atomic Machines team has also created an exciting first device—made possible only through the Matter Compiler™ technology platform—that we will be unveiling to the world soon.Our offices are in Emeryville and Santa Clara, California.About The Role:We are seeking a Senior Data Engineer to join our growing team within the AI and Modeling and Simulation group and support manufacturing processes on our robotic manufacturing platform. The ideal candidate will be responsible for designing, implementing, and maintaining robust data pipelines and infrastructure to ensure the availability, integrity, traceability, and interpretability of manufacturing data. This role involves working closely with Process, Design, and AI engineers to validate manufacturing outcomes and monitor process performance through data-driven insights.Experience in data engineering, data flows, and big-data processing, as well as proficiency in Python, are essential for this role. Importantly, candidates will be expected to have an industry-level understanding of manufacturing processes and sensors, and to be able to work with process engineers on metrology needs and hardware requirements. Experience in data science for manufacturing – e.g., building data-driven predictive pipelines using statistics- and/or ML-based methods – is desirable.This is an excellent career opportunity for a professional with a proven track record of creating and deploying data pipelines in manufacturing environments. The ideal applicant thrives working in a cross-functional environment, unifies and integrates efforts of a highly diverse team, actively engages in the development process, and is excellent at documenting and presenting work products.What You'll Do:Data Engineering & InfrastructureDesign, build, and maintain scalable workflows for data collection, transformation, and storage.Process and analyze structured or semi-structured manufacturing data.Develop data pipelines to process and prepare data for ML model training and data analysis.Develop, customize, and maintain interactive dashboards, reports, and visualizations for experimentation results and business metrics.Ensure real-time data handling for ML applications (e.g., digital twins) and process monitoring.Validation & Quality ControlWork with inspection engineers/technicians to collect datasets and collaborate with process engineers to ensure quality control in manufacturing processes.Experience with real-time data processing frameworks (Apache Kafka, Spark Streaming, etc.)Experienced with BI toolsets and data-visualization frameworks (including Apache Superset) for reporting and analytics. Build validation tests to compare in-house inspection algorithms with commercial tools.Process Monitoring & OptimizationEstablish process monitoring frameworks by creating data storage solutions and implementing structured labeling for process input parameters and associated outputs.Develop online statistics and analytics for process control and optimization. Analyze observable time-series data corresponding to different manufacturing process stages.Documentation & CollaborationWork with process engineers on data collection requirements and communicate those requirements to hardware designers. Collaborate with cross-functional teams, including process engineers, chemical engineers, materials scientists, simulation engineers, software developers, data scientists, and AI engineers, to optimize data workflows and improve operational efficiency.What You'll Need:6 years after Bachelor's / 4 years after Master's of industry experience. A first-principles mindset — you question assumptions, reframe problems from the ground up, and approach challenges with a foundational understanding rather than relying solely on precedent.Proven experience in data engineering, data flows, and big-data processing.Proficiency in Python and programming languages such as SQL.Proficiency in data storage solutions (Data Lakes, Cloud Storage, SQL, NoSQL).Understanding of manufacturing processes, sensors, and process automation.Industry-level experience in guiding and automating data collection and processing in manufacturing environments.Knowledge of DevOps practices. Strong problem-solving skills and ability to work in a collaborative, fast-paced environment.Strong analytical mindset, attention to data quality, and ability to translate complex data into clear insights for both technical and non-technical stakeholders.Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or a related STEM field.Bonus Points For:Experience with real-time data processing frameworks (Apache Kafka, Spark Streaming, etc.) Experienced with BI toolsets and data-visualization frameworks (including Apache Superset) for reporting and analytics.The compensation for this position also includes equity and benefits.Salary Range$170,000—$230,000 USD

Salary : $122,600 - $166,500