Lead Data Engineer
Indexed description
The individual will serve as a bridge between engineering teams and senior leadership, ensuring that software quality insights are accurate, actionable, automated, and consistently communicated.
Responsibilities
Leading the software quality initiative via data monitoring, action identification, and multi-level read out will require multiple aspects:
- Drive Software Quality Data Strategy: Define and manage a unified data framework, ensuring consistent metrics, governance, and data quality across ES Engineering organizations and design centers.
- Build Intelligent Dashboards: Develop automated, real-time dashboards and analytics pipelines that highlight software quality performance, risks, and improvement opportunities.
- Automate Workflows: Create and maintain automated data flows, alerts, and action triggers that enable a proactive, data-driven approach to quality management.
- Coordinate Across Organizations: Align software quality planning and execution across multiple engineering teams, programs, and sites to ensure standardization and transparency.
- Communicate to Leadership: Provide clear, data ‑ driven insights, trends, and recommendations to senior leadership; deliver recurring updates that support decision-making and strategic direction.
- Enable Continuous Improvement: Identify systemic quality issues, streamline processes, and introduce innovative data solutions to strengthen software quality for internal and external customers.
- Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related discipline; advanced degree preferred.
- 8+ years of experience in data engineering, software quality, or software development in complex embedded or regulated environments.
- Deep experience with data engineering tools and technologies (SQL, Python, ETL frameworks, data modeling, cloud platforms).
- Demonstrated expertise designing scalable dashboards and automated analytics workflows (e.g. Tableau).
- Strong understanding of software development life cycles (SDLC), quality processes, and defect management.
WE VALUE
- Advanced degree in Data Science, Computer Science, or a related field
- Experience in aerospace, defense, or safety ‑ critical environments.
- Strong problem-solving skills and the ability to translate complex data requirements into scalable solutions
- Knowledge of data governance and data quality best practices
- Familiarity with DO-178C, CMMI, or similar software quality frameworks.
- Experience integrating data from engineering tools (e.g., JIRA, Git, DOORS, or test automation frameworks).
- Background in predictive analytics, machine learning, or anomaly detection for software quality.
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