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# Hardware Data Analyst

- **Company:** Aperia Technologies
- **Location:** Grand Rapids, MI, United States
- **Experience:** Experienced
- **Salary:** $95,000.0 - $115,000.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Amazon Web Services, Amazon S3, Data Analysis, Data Security, Data Visualization, Firmware, Python (Programming Language), MATLAB, Machine Learning, Power BI, SQL Databases, Tableau (Software), Reliability of Systems, Information Technology, Streamlit Framework, Looker Analytics, Data Pipelines, Amazon Redshift
- **Published:** September 16, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=395c7bcabcffea9e

## About the Role

* Bachelor's degree in Data Science, Computer Science, or a related field.
 * 2-4 years of experience in data analysis, with exposure to hardware-related roles or IoT systems preferred.
 * Proficiency in Python, SQL, MATLAB, or Excel for data analysis and modeling.
 * Demonstrated experience with visualization tools like Quicksight, Tableau, Streamlit, or Looker.
 * Strong understanding of statistical modeling, machine learning, and time-series data analysis.
 * Experience with cloud platforms like AWS (S3, Redshift, Kinesis, IoT Core) or similar is a plus.
 * Strong problem-solving and analytical skills, with the ability to translate data findings into actionable insights.
 * Excellent communication skills to present complex data in a clear, concise manner to technical and non-technical stakeholders.

## Description

* Design and deliver dashboards, reports, and presentations using tools like QuickSight, Tableau, or Python libraries to visualize performance metrics and communicate findings effectively.
 * Continuously monitor and analyze incoming sensor and analytics data to identify trends, anomalies, and potential issues in the field.
 * Use statistical models and machine learning techniques to extract insights and generate actionable recommendations for hardware reliability and performance improvements.
 * Compare outputs from various firmware versions to validate improvements or detect unexpected regressions.
 * Collaborate with hardware engineers, product managers, and data scientists to define and execute data analysis strategies.
 * Support customer support teams by providing data-driven insights to address product performance and anomaly-related inquiries.
 * Proactively identify and propose new tools, methods, or processes to enhance data analysis capabilities and improve operational efficiency.
 * Collaborate with the engineering and data science teams to design and implement data pipelines that ensure reliable ingestion, processing, and storage of large-scale field data.
 * Act as a data advocate within the organization, ensuring teams have the necessary data access and tools to make informed decisions.
 * Collaborate with product development teams to ensure field data is incorporated into the design of future hardware and software features.