Quality Data Analytics - Senior Engineer

- Develop and maintain analytical solutions for customer-quality data, including Warranty, NPS, J.D. Power IQS/VDS/APEAL, and related quality metrics.
- Integrate, cleanse, validate, and transform data from multiple sources to create reliable, analysis-ready datasets. Apply data-quality checks and document assumptions, definitions, and limitations.
- Use statistical analysis, trend analysis, segmentation, and other appropriate analytical methods to identify emerging issues, customer pain points, drivers of performance, and opportunities for corrective action.
- Build and maintain recurring reports, dashboards, and visualizations using tools such as SQL, Excel/VBA, Power BI, and Power Apps, as appropriate to the solution.
- Automate repeatable data-preparation, analysis, and reporting tasks to improve efficiency, consistency, and timeliness while maintaining appropriate validation and controls.
- Translate analytical findings into clear, evidence-based recommendations. Present results, risks, and proposed actions to technical and non-technical stakeholders in review meetings.
- Serve as a technical point of contact for stakeholder questions about data definitions, analytical methods, report results, and interpretation of quality metrics.
- Gather and document stakeholder requirements; incorporate feedback into reporting and analytical solutions while maintaining traceability of changes.
- Perform quality checks on analytical code, datasets, calculations, dashboards, and reports before release; investigate discrepancies and resolve data or logic issues.
- Identify process and technology improvements across Warranty, NPS, and J.D. Power analysis workflows. Develop project proposals that define the problem, scope, approach, expected benefits, and measures of success.
- Coordinate cross-functional contributors, dependencies, and actions to support delivery of analytics and automation projects.
- Coach team members on data preparation, analytical methods, reporting tools, and quality-control practices.
- Maintain current knowledge of relevant analytical methods, reporting technologies, and industry practices.

Education and Experience

- Bachelor’s degree in Engineering, Data Analytics, Statistics, Computer Science, Information Systems, or a related discipline.
- Five or more years of relevant experience in data analytics, business intelligence, quality analytics, or reporting automation.
- Experience working with large or multi-source datasets and delivering recurring analytics or reporting solutions is preferred.

Technical Skills & Knowledge:

- Proficiency in SQL for querying, joining, aggregating, and validating data.
- Hands-on experience with one or more business-intelligence or analytics platforms, such as Power BI, including data modeling, dashboard development, and report publishing.
- Advanced Excel skills, including complex formulas, PivotTables, data modeling, and VBA or equivalent automation techniques.
- Experience developing or supporting workflow and reporting automation using Power Apps or comparable tools.
- Working knowledge of statistical methods and their application to quality and customer-experience problems, such as descriptive analysis, trend analysis, segmentation, correlation, and root-cause investigation.
- Ability to assess data completeness, accuracy, consistency, and validity; investigate anomalies; and communicate data limitations.
- Understanding of customer-quality measures, including NPS, Warranty, and J.D. Power metrics such as IQS, VDS, and APEAL, or the ability to learn and apply them.
- Strong analytical problem-solving skills, with the ability to convert business questions into measurable analyses and actionable recommendations.
- Effective written and verbal communication skills, including the ability to present technical results to stakeholders with varying levels of analytical expertise.
- Proficiency in Microsoft Office applications, particularly PowerPoint, Word, Excel, and Outlook.