Location: Sunnyvale, CA Salary: $56.00 USD Hourly - $76.00 USD Hourly Description:
Our client is currently seeking a Data Engineer (I)
About the Role A Manufacturing Test Quality Data team within global supply chain operations is seeking a Data Analyst to support projects that ensure the quality and reliability of manufacturing and test data.
This position offers the chance to work on high-impact initiatives that strengthen data standards-critical for driving automation, improving root cause analysis, and enabling cross-system insights across worldwide supply chain operations.
What You'll Do
- Data Standards & Quality : Analyze manufacturing test data to define, develop, implement, and measure standards for new and existing data entities.
- Apply industry-standard quality dimensions and perform statistical analysis of test data.
- Dashboarding & Monitoring : Build dashboards to visualize data quality scores across manufacturing partners.
- Develop automated monitoring systems that proactively generate tickets for anomalies and issues.
- AI for Data Standards : Contribute to AI-driven recommendations for data standards, including anomaly detection, rule generation, and intelligent data mapping.
- Log Analysis : Support log analysis, create summaries, and enhance log search capabilities.
Requirements
- Currently pursuing a Bachelor's or Master's in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
- Strong analytical and problem-solving skills with an interest in data quality and data-driven decision-making.
- Proficiency in SQL for querying and manipulation.
- Experience with at least one scripting language (e.g., Python, R ) for data analysis.
- Excellent written and verbal communication skills.
Nice to Haves
- Experience with data visualization tools (Looker, Tableau, Power BI, etc.).
- Exposure to data quality management, data governance, or master data management concepts.
- Basic understanding of machine learning techniques such as anomaly detection or large language models (LLMs).
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