• Lead engagement with senior business stakeholders to define data governance priorities, align on business objectives, and translate needs into actionable data initiatives. • Lead the planning, coordination, and execution of data governance initiatives, ensuring alignment with business priorities, timelines, and governance objectives. • Perform advanced analysis of complex enterprise datasets to identify trends, anomalies, root causes, risks, and opportunities that drive governance decisions, operational improvements, and business outcomes. • Develop executive dashboards, visualizations, and data stories that communicate meaningful insights, governance performance, emerging trends, and business recommendations. • Leverage AI-assisted analysis and productivity tools to improve data discovery, documentation, reporting, and insight generation while ensuring outputs are accurate, explainable, and aligned with data governance standards. • Drive the development, documentation, and maintenance of data governance frameworks, including data quality standards, data lineage, metadata management, and data stewardship models. • Partner with data owners and stewards to ensure accountability for data quality, integrity, and compliance across domains. • Support process assessments and root cause analyses to identify, document, and recommend scalable process and system improvements that enhance data quality and governance. • Support regulatory and compliance initiatives by ensuring adherence to data policies, standards, and controls. • Collaborate with technology, info security, risk, compliance, and business teams to operationalize governance tools and processes. • Mentor junior analysts and contribute to building best practices within the data governance function.

• Own the vision and technical strategy for data engineering at CPI, defining how data is ingested, modeled, and delivered across the organization. • Establish strategy, governance, and architecture to ensure reliability, quality, and health of all data ingestion pipelines. • Lead and develop a team of Data Engineers and Data Analysts responsible for CPI’s data platform and engineering roadmap. • Evolve data governance policies, standards, and quality frameworks across the full data engineering lifecycle. • Scale foundational data infrastructure to accelerate enterprise‑wide AI and generative AI initiatives. • Develop new techniques, reports, and models that improve insights, accessibility, and data‑driven decision making. • Design and develop solutions supporting ingestion, provisioning, and visualization of complex enterprise data for analytics, reporting, and data science. • Create and implement design standards and assurance processes for software, systems, and applications to ensure compatibility and operability of data connections, flows, and storage.

• Lead the maintenance, governance, and continuous improvement of AI Operations dashboards, scorecards, KPIs, and reporting assets. • Ensure operational, consumption, billing, and performance reporting remains accurate, reliable, and actionable. • Track adoption, usage, consumption, cost, billing, capacity, and operational performance trends across AI platforms and services. • Deliver recurring executive, operational, financial, and consumption reporting packages. • Analyze operational and financial data to identify trends, risks, opportunities, and areas requiring attention. • Investigate reporting discrepancies, consumption anomalies, and unexpected usage patterns to determine root causes and recommend corrective actions. • Perform deep-dive analysis of AI operational, financial, adoption, and consumption data. • Correlate data from multiple sources to provide meaningful insights and recommendations. • Develop executive-ready summaries, trend analysis, and operational performance narratives. • Oversee consumption monitoring, utilization reporting, billing visibility, and cost transparency processes. • Support forecasting, budgeting, chargeback, and show back reporting activities. • Identify opportunities to improve operational efficiency and optimize resource utilization. • Partner with Finance to support AI consumption reporting and cost management activities. • Maintain reporting standards, data quality controls, metric definitions, support documentation, and reporting governance processes. • Ensure consistency and integrity of operational, financial, and consumption reporting. • Support operational audits, compliance reporting, and governance reviews. • Partner with Operations, Support, Engineering, and Finance teams to resolve reporting questions, data issues, and operational reporting needs.

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software and V-BAT and X-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description:We’re looking for a Staff Engineer to lead V-BAT’s fleet data analysis efforts. The V-BAT system produces rich data from real-world flight tests, fielded aircraft, production activity, and simulation runs. You will own the analysis pipelines, tooling, standards, and metrics that help the team understand fleet-wide system performance derived from information from software and hardware systems. You will facilitate deep studies into flight-critical sensor performance, navigation, communications, and other key aircraft behaviors. You will work closely with DevOps, Flight Software, Flight Test, Production, Quality, and Operations teams to turn complex aircraft data into actionable engineering insight. What you'll do: - Lead the technical strategy for V-BAT fleet data analysis across fielded aircraft, flight test, simulation, production, and quality workflows - Own and improve the pipelines that transform raw flight, simulation, and fleet data into reliable engineering metrics, reports, and analysis products - Conduct fleet-wide studies to identify trends in hardware quality, system performance, reliability, and operational behavior - Analyze flight-critical sensor performance, GNSS-denied navigation performance, communications behavior, and other aircraft subsystems that are critical to mission success - Standardize the team’s analysis methods across online tools, Jupyter notebooks, automated Python scripts, and legacy Matlab workflows - Define best practices for analysis methods, data review, documentation, validation, reproducibility, and contribution workflows - Build automated Python workflows that make high-value metrics easily accessible to engineering, production, quality, and leadership teams - Partner with DevOps to build, deploy, maintain, and scale the infrastructure required for automated analysis pipelines and dashboards - Work with Software, GNC, Embedded, Flight Test, Systems, Production, Quality, and Operations teams to define metrics that reflect aircraft performance and product health - Support anomaly investigations, root-cause analysis, release readiness, production quality improvements, and customer-impacting fleet investigations with rigorous data analysis - Mentor engineers on effective data analysis practices and raise the quality bar for data-driven engineering decisions across the V-BAT team Required qualifications: - 5+ years of relevant experience with a Bachelor’s degree in Computer Science, Data Science, or a related technical field - Strong Python skills and experience building data analysis tools, automated analysis workflows, or data pipelines - Experience analyzing data from fielded physical products such as aerospace systems, automotive systems, robotics platforms, commercial electronics, IoT devices, industrial equipment, or similarly complex real-world systems - Experience working with large, messy datasets from deployed products, test events, simulations, production systems, or operational environments - Ability to translate ambiguous engineering questions into structured analysis, sound conclusions, and clear recommendations - Strong communication skills and the ability to influence cross-functional engineering, production, quality, and operations teams Preferred qualifications: - Experience with flight test data, aircraft telemetry, UAVs, robotics, autonomy, embedded systems, GNC, navigation systems, communications systems, or aerospace sensor suites - Experience with Matlab, Jupyter, or similar analysis and visualization tools - Experience building production-quality dashboards, automated reports, data products, or fleet-health monitoring systems - Experience with databases, cloud storage, data lake architectures, time-series databases, telemetry systems, or data cataloging - Experience with anomaly detection, trend analysis, statistical process control, reliability analysis, regression detection, or automated validation of complex systems #LI-SM1 #LD Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. ### Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.