Description
At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work — and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history.Northrop Grumman Mission Systems seeks a Quality Engineer to lead sector‑wide data analytics. The Quality Engineer is responsible for leveraging data analytics to identify quality trends, emerging risks, and opportunities for continuous improvement across programs and functional organizations. This role serves as a strategic partner to Quality leadership by transforming complex data into actionable insights that drive risk-based decision making and improve organizational performance.
The successful candidate will collect, analyze, and interpret quality data from multiple enterprise systems to identify systemic issues, develop meaningful performance metrics, and recommend areas requiring increased Quality Engineering focus. This position requires strong analytical skills, curiosity, and the ability to communicate technical findings to both engineering and executive leadership.
This position will be based full-time on-site in Linthicum, MD.
What You'll Get To Do:
Analyze large and complex datasets from quality, manufacturing, engineering, supplier, and program sources to identify trends, systemic issues, and emerging risks.
Participate in development of dashboards, scorecards, and visualizations that provide meaningful insights into quality performance.
Perform statistical analyses to identify recurring defects, process variation, escape trends, and leading indicators of risk.
Conduct risk assessments to prioritize Quality Engineering resources toward the highest-impact opportunities.
Develop predictive metrics and leading indicators to support proactive quality management.
Partner with Program Quality Engineers, Manufacturing Quality, Supplier Quality, Mission Assurance, and Engineering organizations to investigate trends and recommend corrective actions.
Evaluate enterprise quality data to identify opportunities for process improvements and standardization.
Support executive leadership by preparing data-driven reports, presentations, and recommendations.
Develop and maintain automated reporting solutions using business intelligence and analytics tools.
Assist with root cause investigations by identifying data patterns and contributing analytical support.
Ensure data integrity by validating data sources and improving quality data management processes.
Support continuous improvement initiatives through measurement, analysis, and performance tracking.
Strong analytical, problem-solving, and critical thinking skills.
Excellent written and verbal communication skills.
Basic Qualifications:
Bachelor's degree in Engineering, Computer Science, Data Analytics, Mathematics, Statistics, or a related STEM discipline and 8 years of relevant experience; or a Master's degree and 6 years of relevant experience or PhD and 4 years of relevant experience.
Experience analyzing large datasets to identify trends and support decision making.
Experience with data visualization tools such as Power BI, Tableau, or similar platforms.
Experience using Microsoft Excel for advanced data analysis (Power Query, Pivot Tables, formulas, etc.).
Experience presenting technical information to various levels of leadership.
Preferred Qualifications:
Experience in aerospace, defense, manufacturing, or other highly regulated industries.
Experience with SQL or database querying.
Familiarity with statistical analysis techniques and process capability.
Knowledge of AS9100, ISO 9001, or other Quality Management Systems.
Understanding of risk management methodologies including FMEA, PFMEA, DFMEA, and FRACAS.
Experience developing executive-level dashboards and KPIs.
Lean Six Sigma Green Belt or Black Belt certification.
ASQ Certified Quality Engineer (CQE) certification.
