Data Engineer Sr Staff - Level 5

Lockheed Martin Aeronautics Quality & Mission Success (Q&MS) is seeking a Senior Staff Data Engineer to lead the architecting and implementation of the Q&MS data system and its supporting data-management plan. Reporting into Quality Performance Management and aligned to the Q&MS Digital Transformation Director, the role establishes the reference architecture, common data models, and governance patterns that consolidate fragmented Q&MS data into governed, value-creating data products fit for reporting, decision support, and approved AI uses. The architect works across a federated operating model spanning multiple participating organizations, replacing fragmented pipelines and definitions with a coherent, measurable data system, and interfaces with the Aeronautics data office, 1LMX, IT, and other intra- and extra-functional teams.

Key Responsibilities

• Establish and maintain the target-state reference architecture across the data-system lifecycle, along with the current-to-target transition architecture.
• Author and maintain common and canonical data models; reconcile them with applicable enterprise and business-unit models to enable semantic interoperability and shared reuse.
• Own the multi-year data-architecture roadmap that feeds the annual data portfolio plan; sequence capability increments, dependencies, and shared-capability alternatives before dev/test/build sequences.
• Author functional data-architecture command media aligned to applicable enterprise data-management, governance, stewardship, and data-quality direction; prepare architecture-decision content for senior functional leadership and higher-tier governance bodies.
• Define architectural patterns and certification criteria for governed data products, pipelines, and interfaces so participating intra-Q&MS organizations can adopt shared capabilities without divergent localized implementations.
• Translate organizational data-management maturity gaps into architectural investments carried in the Q&MS data-system roadmap.
• Advise on governance approach for AI enablement — traceability, lineage, and fitness-for-use for approved AI agentic use cases.
• Serve as the architecture interface to the enterprise data office, corporate IT, and corporate data and AI enablement organizations; elevate matters beyond functional authority through the applicable interface.
• Mentor Staff and Senior data engineers, modelers, and technical stewards across participating organizations.

Delivers full-stack data solutions across the entire data processing pipeline. This relies on systems engineering principles to design and implement solutions that span the data lifecycle to: collect, ingest, process, store, persist, access, and deliver data at scale and at speed. It includes knowledge of local, distributed, and cloud-based technologies; data virtualization and smart caching; and all security and authentication mechanisms required to protect the data.Build data pipelines that clean, transform, and aggregate unorganized data into databases or data sources that are ready for analysis; Design and implement data solutions by defining functional capabilities, security, back-up, and recovery specifications; Work through all stages of a data solution lifecycle, e.g., analyze / profile data, create conceptual, logical and physical data model designs, architect and design ETL, reporting and analytics; Maintain data systems performance by identifying and resolving production and application development problems; calculating optimum values for parameters; evaluating, integrating, and installing new releases; Define standards, best practices, and certification processes for data objects

• Bachelor's degree in a related technical discipline plus 14+ years in data architecture or enterprise data management
• Demonstrated ownership of enterprise or functional data-architecture strategy, reference architectures, and common data models
• Advanced conceptual, logical, and physical data modeling for complex, cross-organizational domains
• Demonstrated authorship of governance and architecture artifacts — standards, models, architectural decision records — and experience presenting to senior governance bodies
• Ability to obtain and maintain a U.S. DoD Secret clearance (U.S. citizenship required)

• Databricks (Unity Catalog, Delta, Lakehouse patterns)
• Metadata, lineage, glossary, and data-product certification design
• Common data modeling and semantic interoperability across federated organizations
• Familiarity with controlled command-media authoring in a regulated enterprise
• Quality data-model domain experience (nonconformance, corrective action, assessment, inspection)
• Relevant certifications (e.g., CDMP, TOGAF, Databricks Data Engineer, Security+)