Sr. Principal Engineer; Data Readiness Agile Product Owner (Onsite)

RTX•East Hartford, CT

5d•Onsite

About The Position

The Data Readiness Agile Product Owner will lead an Agile team responsible for preparing, standardizing, and enabling business process data to support advanced Digital/Artificial Intelligence (AI) capabilities across Military Engine Sustainment Operations. This role is central to defining and maturing Data‑as‑a‑Product practices to ensure data is high‑quality, trusted, reuseable and ready for enterprise AI integration. As a P5‑level senior professional, the role requires expert domain leadership, the ability to influence across functional boundaries, and mastery of data, digital, and business processes. The position will drive strategic data readiness outcomes, manage complex and ambiguous problem spaces, and collaborate closely with Engineering, Sustainment Strategy, Digital Technology, Enterprise Data organizations, and business leaders.

Requirements

Bachelor’s degree with 10+ years of relevant experience, or an Advanced Degree with 7+ years of experience in engineering, digital technology, analytics, data management, or related discipline.
Demonstrated experience as a Product Owner, Agile Lead, Scrum Master, or equivalent leadership role.
Strong knowledge of Data as a Product, data governance, data lifecycle management, and data quality principles.
Working knowledge of AI, machine learning concepts, and digital technologies.
U.S. citizenship required due to government/DoD program access requirements.

Nice To Haves

Experience in Aerospace, Military Engines, Sustainment Operations, or related high‑reliability engineering environments.
Hands‑on experience with enterprise data platforms, data cataloging, data lineage tooling, or cloud‑based analytics ecosystems.
Background in process modeling, system integration, digital transformation, or AI/ML model enablement.
Certified Scrum Product Owner (CSPO), SAFe Product Owner/Product Manager, or equivalent Agile certification.
Experience translating business strategy into data‑centric product roadmaps and measurable execution plans.
Demonstrated ability to create new processes, standards, and solutions using industry best practices — aligned to P5 expectations for innovation and strategic impact.
A degree in a data- or analytics-related field

Responsibilities

Serve as the Product Owner for an Agile team focused on preparing operational and sustainment data for digital and AI use cases, defining the product vision, roadmap, and value delivery.
Manage and prioritize the backlog
Lead development and implementation of Data‑as‑a‑Product frameworks, including ownership models, data quality expectations, metadata standards, and lifecycle management.
Partner with cross‑functional stakeholders to identify business processes and data sets critical to AI readiness, including data profiling, gap assessment, cleansing, and transformation requirements.
Be the voice of the business, gathering requirements, translating stakeholder needs into data requirements, and ensuring data solutions meet user and regulatory needs
Translate business needs into prioritized user stories, acceptance criteria, and sprint objectives; ensure consistent delivery of measurable value aligned to business and sustainment strategies.
Champion data quality, lineage, governance, and accessibility across sustainment operations; drive adoption of enterprise data platforms and standards.
Collaborate with AI and digital engineering teams to ensure data products support machine learning, predictive analytics, automation, and digital workflow modernization.
Work closely with data architects, data engineers, data scientists, analysts, and other product owners to ensure data is fit for purpose before it enters analytics or product pipelines
Apply advanced problem‑solving and analytical skills to ambiguous, multi‑disciplinary data challenges, providing innovative and scalable solutions consistent with P5 expectations.
Communicate program status, risks, and strategic impacts to senior leadership and external stakeholders; influence decisions that affect operations and long‑term data strategy.
Participate in Sprint Planning, Stand-ups, Sprint Reviews, and Retrospectives, clarifying backlog items, resolving conflicts, and ensuring alignment between data readiness and product
Analyze feedback and sprint outcomes to refines data readiness processes, improves backlog grooming, and adapts to changing business or data landscapes
Coach and mentor team members, fostering a high‑performing and collaborative data‑driven culture.