AI Adoption Data Specialist (mid-career)

Lockheed Martin•Fort Worth, CO

1d•$108,800 - $216,890•Remote

About The Position

The Lockheed Martin Artificial Intelligence Center (LAIC) seeks a high-energy AI Adoption Data Specialist with a strong background in data engineering, generative AI, and developer enablement. You will be part of a multidisciplinary team of software engineers, product managers, and AI/ML experts focused on accelerating the adoption of developer AI tools across the enterprise. This role blends data pipeline engineering with technical advocacy and strategic change management—you’ll design and maintain the data infrastructure that powers AI adoption measurement, champion the use of AI-powered developer tools, and lead data-driven initiatives that ensure these solutions deliver measurable impact for Lockheed Martin teams. The ideal candidate combines strong data engineering fundamentals with an understanding of how data infrastructure supports organizational AI maturity and enterprise-wide transformation.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related STEM field.
5+ years of professional experience in data engineering, data pipeline development, AI strategy, developer relations, or technical program management.
Proficiency in Python and SQL for data manipulation, transformation, and pipeline automation.
Hands-on experience with data pipeline/orchestration tools (Flyte, AirByte, Apache Airflow, Prefect, Luigi, or equivalent).
Experience with relational databases (PostgreSQL, MySQL, or similar).
Strong technical understanding of generative AI, developer tools, and modern software development workflows.
Excellent communication and storytelling skills, with the ability to engage both technical and executive audiences.

Nice To Haves

Experience with data pipelines in defense, aerospace, or highly regulated industries.
Familiarity with AI/ML data preparation workflows, feature engineering, and training data management.
Experience with streaming data platforms (Kafka, Spark Streaming, or equivalent).
Knowledge of data visualization tools (Tableau, Power BI, or Grafana) for dashboard integration.
Advanced credentials in AI ethics, MLOps, or enterprise architecture.
Hands-on experience with AI-assisted development tools (e.g., GitHub Copilot, ChatGPT, internal LLMs).
Proven ability to build and sustain technical communities.
Exposure to containerization (Docker, Kubernetes) and CI/CD for data pipeline deployment.
Experience with API development (REST, GraphQL) for data service layers.
Familiarity with Lockheed Martin internal platforms (Navigator, Genesis, AI Factory)

Responsibilities

Data Pipeline Design & Development
Architect and implement end-to-end data pipelines that ingest, transform, and deliver data from diverse enterprise sources to support AI adoption programs and analytics.
Design ETL/ELT workflows using Python, SQL, Flyte, and/or similar orchestration tools to process structured and unstructured data at scale.
Build and maintain data models optimized for AI/ML training datasets, adoption metrics, and executive dashboards.
Develop automated data quality checks, validation rules, and monitoring to ensure pipeline reliability and data integrity.
AI Adoption Analytics & Measurement
Create data infrastructure supporting AI adoption KPIs—including training completion rates, tool utilization, and organizational readiness metrics—across 120,000+ employees.
Build pipelines that aggregate and normalize data from LM Navigator, Genesis, and other internal AI platforms to provide unified adoption reporting.
Synthesize adoption metrics, developer feedback, and market trends into executive-ready reports and decision packages.
Develop real-time and batch data feeds for leadership dashboards that track AI maturity across business areas and programs.
Data Integration, Governance & Security
Integrate data from multiple internal platforms, HR systems, learning management systems, and AI tools into cohesive, AI-ready datasets.
Document data lineage, maintain data dictionaries, and enforce governance standards across all pipeline outputs.
Champion AI ethics, security, and trust principles within data workflows and the broader developer ecosystem.
Collaborate with cybersecurity and IT teams to ensure secure data transfer and storage within on-premises and classified environments.
Community & Ecosystem Development
Build and sustain a vibrant AI developer community through events, hackathons, and forums focused on data-driven AI adoption.
Serve as a bridge between developers, data teams, product teams, and leadership to ensure alignment on priorities and roadmap.
Contribute to the AI Factory ecosystem by developing reusable data components, integration patterns, and shared services.
Partner with engineering leaders to identify tool and solution gaps, and influence product direction based on developer feedback and adoption data.
Provide technical guidance to junior team members on data engineering best practices.