AI Analytics Technical Lead

Basic Qualifications

Bachelor's degree in Software Engineering, or related Science, Technology, Engineering or Mathematics field, plus a minimum of 10 years of relevant experience; or Master's degree, plus 8 years relevant experience.

CLEARANCE REQUIREMENTS:

Ability to obtain a Department of Defense Secret security clearance is required at time of hire. Applicants selected will be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required.

Responsibilities for this Position

The Opportunity

General Dynamics Mission Systems operates AI-enabled capabilities across multiple platforms, providers, applications, and security environments. As human and agentic usage expands, leaders and operators need timely, trusted visibility into consumption, capacity, performance, adoption, value, and financial investment.

The AI Analytics Technical Lead is a hands-on senior individual contributor who will connect governed source data, enterprise data platforms, semantic standards, and owner-approved metrics into consistent and decision-ready analytical products. The role leads technical delivery through a matrixed team without direct-report responsibility.

The role owns AI analytics integration, data contracts, shared transformations, quality controls, reporting-pipeline design, and analytical products assigned to Innovation & Engineering. It partners with, but does not replace, the teams that own Snowflake, the enterprise data warehouse, the enterprise ontology, source-application production, source systems, business metric definitions, and financial policy.

What You Will Do

Translate AI investment and operational decision needs into phased requirements, integration architecture, interfaces, acceptance criteria, and a technical roadmap.

Connect approved telemetry, usage, capacity, cloud billing, application, agent, identity, organization, program, and financial-reference data through governed interfaces to enterprise data platforms.

Co-design and implement versioned data and telemetry contracts that preserve identity, application, project, program, requested and served model, provider, environment, consumption, and traceable investment inputs.

Build analytics-ready fact models, transformations, lineage, quality controls, reconciliation, replay, recovery, and historical reproducibility on owner-approved enterprise platforms.

Establish one governed reporting path across approved dashboards, reports, emails, extracts, alerts, and interfaces, with consistent results and visible data status.

Implement and publish metric definitions approved by value-stream and business owners without creating conflicting local definitions or duplicate systems of record.

Produce technical readiness evidence, including service objectives, monitoring, reconciliation, recovery tests, and rollback criteria, while source and platform owners retain production authority.

Lead engineers, source owners, enterprise data teams, ontology owners, platform teams, security, privacy, Finance, and metric owners around a shared roadmap and evidence-based decisions.

First-Year Outcomes

Baseline priority sources, reports, owners, definitions, interfaces, data quality, attribution coverage, reporting timeliness, and manual effort.

Publish an approved integration roadmap, report catalog, interface requirements, acceptance criteria, and ownership map.

Put priority integrations and reports into repeatable operation on approved enterprise platforms with automated quality, reconciliation, monitoring, and recovery evidence.

Deliver a governed interface from an initial AI-enabled source application and reusable patterns for additional sources, with consistent operational and executive reporting across approved surfaces.

What We Are Looking For

You are a senior software, data-integration, and analytics technical leader who remains hands-on. You can build critical transformations and reporting components, lead across organizational boundaries, and explain evidence and tradeoffs to senior leaders. You know how to create a trusted path from source data to decisions while respecting platform, source, ontology, metric, privacy, and policy ownership.

Required

Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, Statistics, or a related technical discipline with at least 10 years of relevant experience, or a master's degree with at least 8 years of relevant experience. An equivalent combination may be considered in accordance with GDMS requirements.

Demonstrated experience as a technical lead or principal engineer for a production analytics, telemetry, data-integration, or Financial Operations capability, including matrixed leadership.

Strong SQL and Python skills, with hands-on experience building governed transformations, analytical fact models, data-quality controls, and reporting pipelines on enterprise data platforms.

Experience with versioned data contracts, application programming interfaces, stable identifiers, idempotent processing, replay, reconciliation, schema evolution, observability, recovery, parallel validation, and controlled cutover.

Experience integrating cloud billing, platform telemetry, model gateways, identity, organizational data, program metadata, and financial-reference data through governed interfaces.

Experience with semantic and ontology mappings, effective-dated dimensions, lineage, metric versioning, and reproducible reporting.

Experience applying privacy controls to individual-level data, including row-level access, de-identification, retention, aggregation thresholds, and approved management-reporting boundaries.

Understanding of AI platform mechanics, including tokens, context, caching, inference, model tiers, routing, rate limits, latency, utilization, and agentic execution.

Preferred

Experience in defense and aerospace, including controlled unclassified information and classified-environment data handling.

Experience with AI or machine-learning platform operations, model-serving infrastructure, or large language model deployment at enterprise scale.

Experience with Snowflake, Azure, Databricks, PostgreSQL, event-driven and lakehouse patterns, Denodo, Power BI, continuous integration and delivery, data observability, and performance testing.

Experience integrating enterprise ontology, semantic models, identity services, model management, and governed application programming interfaces into analytical products.

Experience with automated operational and financial reporting, cloud-consumption analytics, agent attribution, anomaly detection, forecasting, or scenario modeling.

Why This Role Matters

AI investment and operational decisions depend on trusted information. This role will provide the technical integration and reporting leadership needed to connect source systems, enterprise platforms, semantic standards, and owner-approved metrics into timely and defensible decision products without creating a competing data platform.

Our Commitment to You:

An exciting career path with opportunities for continuous learning and development.

Research oriented work, alongside award winning teams developing practical solutions for our nation’s security

Flexible schedules with every other Friday off work, if desired (9/80 schedule)

Competitive benefits, including 401k matching, flex time off, paid parental leave, healthcare benefits, health & wellness programs, employee resource and social groups, and more

See more at gdmissionsystems.com/careers/why-work-for-us/benefits