Staff Engineer Data

Northrop Grumman•Linthicum Heights, MD

1d•$169,000 - $253,600•Hybrid

About The Position

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. At the heart of Defining Possible is our commitment to missions. In rapidly changing global security environments, Northrop Grumman brings informed insights and secure technological solutions to enable strategic objectives. We’re looking for innovators who can help us keep building on our wide portfolio of secure, affordable, integrated, and multi-domain systems and technologies that fuel those missions. By joining in our shared mission, we will support yours of expanding your personal network and developing skills, whether you are new to the field or an industry thought leader. At Northrop Grumman, you will have the resources, support, and team to do some of the best work of your career. Mission Systems CIDO is seeking a Data Architect to lead the development and governance of the sector’s data strategy. This role will define authoritative data models, standards, and integration frameworks across manufacturing, engineering, quality, supply chain, and program domains—ensuring that sector data is consistent, reusable, and aligned with enterprise platforms and tools. The Data Architect will partner closely with functional stakeholders and CIDO technical leadership to design scalable, governed data solutions that reduce fragmentation, improve interoperability, and enable mission‑critical analytics and AI capabilities. A hybrid work schedule may be considered for this position; please note that the selected candidate will be required to work on-site 3 days per week at our Linthicum, MD campus. The Data Architect will lead the architecture and development of the Mission Systems (MS) sector data strategy across functions and programs, aligned to enterprise data strategy and enterprise tools. The role establishes repeatable, scalable frameworks, policies, and data standards; designs authoritative data models and contracts; and ensures reliable, governed data flows that ground AI and analytics. Working closely with the CIDO Systems Architect and AI Architect, the Data Architect meets with customers to determine the best course of action and delivers sector‑wide solutions that are reproducible and scalable.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems and 12+ years of experience, or Master’s degree in Computer Science, Engineering, Information Systems and 10+ years of experience.
The selected candidate will be required to obtain a U.S. Government Secret level security clearance and a Special Access Program (SAP/SAR) clearance; maintaining the required clearances will be a condition of continued employment.
Experience in data architecture, enterprise data modeling, and data platform engineering across multiple domains.
Hands‑on experience designing governed data models and contracts, strong SQL and schema design, and a familiarity with event streaming and batch ETL/ELT.
Experience with metadata/catalog tools, lineage mapping, and data quality management, and an ability to operationalize policies at scale.
Containerization/orchestration experience (Docker/Kubernetes), CI/CD for data pipelines, and secure software supply chain practices.
Experience implementing data solutions in sovereign/classified or air‑gapped environments, and an understanding of auditing, logging, and compliance.

Nice To Haves

Experience with Palantir Foundry ontology and analytics pipelines, and an ability to align sector data models to canonical enterprise structures.
Manufacturing and quality/QN analytics domain experience (shop‑floor data, SPC, defect/QN processes, root‑cause/corrective action).
Familiarity with ERP/SAP integration patterns, MES/PLM/ERP data domains, and cross‑system data harmonization.
Experience establishing sector‑wide data standards, stewardship models, and governance councils.
Exposure to supporting AI/ML workloads (feature stores, RAG grounding, data readiness) and measurement of model/data ROI.

Responsibilities

Define and maintain the MS sector data strategy and reference architecture, aligning with enterprise standards and tools (e.g., canonical ontologies, ERP/SAP integration, M365, Palantir Foundry).
Establish repeatable, scalable frameworks for data ingestion, normalization, enrichment, and publication; create reusable patterns and data contracts (APIs, schemas).
Design governed data models for key domains (manufacturing, quality/QN, supply chain, engineering) and prevent shadow ontologies by grounding models in the authoritative enterprise ontology.
Lead data quality management (DQ rules, profiling, remediation), metadata/catalog adoption, lineage mapping, and auditability; define policies for citation and source traceability in AI use.
Architect secure data pipelines and storage across sovereign, classified, and disconnected environments; implement observability, reliability, and lifecycle management.
Partner with the AI Architect to ground Retrieval‑Augmented Generation (RAG) and agent workflows in authoritative sector data; ensure data readiness, freshness, and guardrails for AI/ML.
Collaborate with the Systems Architect to align solution designs with platform constraints and delivery patterns; provide architecture decision records (ADRs) and design reviews.
Meet with program customers and functional stakeholders to capture requirements, evaluate solution paths, and recommend approaches that balance speed, governance, and maintainability.
Define and socialize data policies (access, stewardship, retention, classification) and operating processes (intake, review, approval) to scale adoption across programs.
Establish KPIs and ROI metrics (data quality, timeliness, reuse, cycle‑time impact) and drive continuous improvement; create playbooks and templates for rapid onboarding.