Job Summary
We are seeking a skilled and experienced Quality Data Analyst to join our team. This role will focus on leveraging data analysis and advanced techniques to support our Electrical Hardware Engineering, Warranty Analytics, KPI Automation, Design Assessment, and AI Adoption initiatives within the Automotive / Technology industry. The ideal candidate will possess a strong analytical background and a passion for driving data-driven decisions.
Key Responsibilities
This role will be responsible for a range of tasks including data analysis, visualization, and reporting to support quality initiatives. The candidate will be expected to identify trends, anomalies, and opportunities for improvement within warranty data, cost trends, and design performance. They will also be involved in the automation of KPI reporting and the implementation of AI-assisted analytics to enhance predictive quality and drive corrective action management.
Required Skills & Qualifications
- Data Analysis: Demonstrated ability to extract, clean, and analyze large datasets to identify meaningful insights and trends.
- Data Visualization: Proficiency in creating compelling and informative data visualizations using tools such as Power BI and Excel Analytics to communicate complex information effectively.
- Dashboarding & KPI Reporting: Experience in designing and developing interactive dashboards and automated KPI reports to track key performance indicators and monitor quality metrics.
- Data Storytelling: Ability to translate data insights into clear and concise narratives that resonate with stakeholders and drive informed decision-making.
- SQL: Strong proficiency in SQL for data extraction, manipulation, and analysis.
- Python: Experience using Python for data analysis, scripting, and automation.
- Databricks: Familiarity with Databricks for data processing and analytics.
- Power Automate: Experience with Power Automate for workflow automation and data integration.
- Warranty Data Analytics: Expertise in analyzing warranty data to identify root causes of failures, predict future trends, and optimize product designs.
- Cost Trend Analysis: Ability to analyze cost trends to identify areas for cost reduction and process improvement.
- KPI Automation: Experience in automating KPI reporting and data collection processes.
- Multi-Source Data Integration: Ability to integrate data from multiple sources to create a comprehensive view of quality performance.
- AI-Assisted Analytics: Experience leveraging AI and Generative AI techniques to enhance data analysis and predictive modeling.
- Predictive Quality: Ability to apply statistical methods and machine learning algorithms to predict product quality and identify potential issues proactively.
- Anomaly Detection: Experience in identifying and investigating anomalies in data to uncover potential quality problems.
- Root Cause Analysis: Expertise in conducting root cause analysis to identify the underlying causes of quality issues.
- Corrective Action Management: Ability to develop and implement corrective action plans to address quality issues and prevent recurrence.
- DFMEA, DRBFM, APQP: Familiarity with DFMEA, DRBFM, and APQP methodologies for design and process improvement.
- Statistical Tools: Proficiency in using various statistical tools for data analysis and quality control.
- Structured Problem Solving: Ability to apply structured problem-solving techniques to identify and resolve quality issues.
- Process Optimization: Experience in identifying and implementing process optimization initiatives to improve quality and efficiency.
- Lean & Six Sigma: Knowledge of Lean and Six Sigma principles for continuous improvement.
- Quality Tools: Familiarity with various quality tools and techniques for data analysis and problem-solving.
- Issue Management: Ability to effectively manage and track quality issues to resolution.
- Warranty Management: Experience in managing warranty claims and analyzing warranty data.
- Field Quality Analysis: Ability to analyze field quality data to identify trends and patterns.
- Design Assessment: Ability to conduct design assessments to identify potential quality risks and improve product designs.
- Design Integrity: Understanding of design integrity principles and their impact on product quality.
- Design Robustness: Ability to evaluate and improve the design robustness of products.
- Lessons Learned Deployment: Ability to capture and deploy lessons learned from quality issues to prevent recurrence.
- Analytical Skills: Strong analytical skills with the ability to interpret data and draw meaningful conclusions.
- Problem-Solving Skills: Excellent problem-solving skills with the ability to identify and resolve complex quality issues.
- Data Interpretation Skills: Ability to accurately interpret data and translate it into actionable insights.
- Communication Skills: Excellent communication skills with the ability to effectively communicate technical information to both technical and non-technical audiences.
- Stakeholder Management: Ability to build and maintain relationships with key stakeholders.
- Leadership-Ready Presentation Skills: Ability to deliver compelling and persuasive presentations to senior management.
Qualifications: Bachelor’s degree in Electrical Engineering, Electronics Engineering, Automotive Engineering, Data Analytics, Computer Science, or a related engineering discipline.
What We Offer
Experience Required: 5 - 10 years of relevant experience.
Salary:
