Quality Data Analyst – Electrical Hardware Engineer

Job Summary

We are seeking a skilled and experienced Quality Data Analyst to join our Electrical Hardware Engineering, Warranty Analytics & Quality Automation team. This role will be instrumental in leveraging data to drive quality improvements, identify trends, and ensure the reliability of our products. The ideal candidate will possess a strong analytical background, a passion for data-driven decision-making, and a commitment to continuous improvement within the automotive and technology industries.

Key Responsibilities

The Quality Data Analyst will be responsible for a range of tasks related to data analysis, reporting, and process improvement. This includes conducting in-depth analysis of warranty data, identifying cost trends, and developing automated reporting solutions. The role will also involve collaborating with cross-functional teams to implement corrective actions and preventative measures to enhance product quality and reliability.

Required Skills & Qualifications

To be successful in this role, candidates must possess a combination of technical skills, analytical abilities, and a strong understanding of quality methodologies. Specific requirements include:

• Analytical Skills: Demonstrated ability to analyze complex data sets, identify patterns, and draw meaningful conclusions.
• Problem-Solving Skills: Proven ability to approach complex problems systematically, identify root causes, and develop effective solutions.
• Data Analysis: Proficiency in data analysis techniques, including statistical analysis and data mining.
• Data Visualization: Ability to create clear and compelling data visualizations to communicate insights effectively.
• Dashboarding: Experience in designing and developing interactive dashboards to monitor key performance indicators (KPIs).
• KPI Reporting: Ability to generate regular reports on KPIs and present findings to stakeholders.
• Data Storytelling: Ability to translate data into a narrative that is easy to understand and actionable.
• SQL: Strong proficiency in SQL for data extraction, manipulation, and analysis.
• Python: Experience using Python for data analysis and automation.
• Databricks: Familiarity with Databricks for large-scale data processing and analytics.
• Power Automate: Experience with Power Automate for automating data-related tasks and workflows.
• Warranty Data Analytics: Expertise in analyzing warranty data to identify trends and opportunities for improvement.
• Cost Trend Analysis: Ability to analyze cost data to identify trends and potential cost savings.
• KPI Automation: Experience in automating the generation and distribution of KPIs.
• Multi-Source Data Integration: Ability to integrate data from multiple sources into a unified view.
• AI-Assisted Analytics: Experience leveraging AI to enhance data analysis and predictive modeling.
• Predictive Quality: Knowledge of predictive quality techniques to anticipate and prevent quality issues.
• Anomaly Detection: Ability to identify unusual patterns or outliers in data that may indicate quality problems.
• Generative AI: Familiarity with Generative AI techniques for data analysis and problem-solving.
• Issue Management: Experience in tracking and resolving quality issues.
• Root-Cause Analysis: Proficiency in conducting root-cause analysis to identify the underlying causes of quality problems.
• Corrective Action Management: Ability to develop and implement effective corrective actions to prevent recurrence of quality issues.
• DFMEA: Understanding of DFMEA (Design Failure Mode and Effects Analysis) principles.
• DRBFM: Understanding of DRBFM (Design Review Based on Failure Mode) principles.
• APQP: Understanding of APQP (Advanced Product Quality Planning) principles.
• Statistical Tools: Proficiency in using various statistical tools for data analysis and quality improvement.
• Structured Problem Solving: Ability to apply structured problem-solving methodologies to address quality challenges.
• Power BI: Proficiency in Power BI for data visualization and reporting.
• Excel Analytics: Strong Excel skills for data analysis and reporting.
• Process Optimization: Ability to identify and implement process improvements to enhance quality and efficiency.
• Six Sigma: Knowledge of Six Sigma methodologies for process improvement.
• Quality Tools: Familiarity with various quality tools and techniques.
• Lean: Understanding of Lean principles for waste reduction and process improvement.

Education: A Bachelor’s degree in Electrical Engineering, Electronics Engineering, Automotive Engineering, Data Analytics, Computer Science, or a related engineering discipline is required.

Experience: The ideal candidate will have 5-10 years of experience in a related field, demonstrating a strong track record of success in data analysis and quality improvement. This level of experience is expected to provide a solid foundation for independent work and leadership within a team.

What We Offer

Salary: