AI Governance Analyst 1

Datadog•New York, NY

11h•Hybrid

About The Position

Datadog is hiring a Governance Analyst to focus on the unique challenges of AI Orchestration. This role requires a blend of technical acumen, policy understanding, and process design skills to ensure AI systems operate within lawful and policy-compliant boundaries by design. This person will be instrumental in moving the governance model from process review to continuous, embedded constraint. The small, high-impact Applied Privacy & Governance team connects engineering, legal, and product development teams to scale governance, embed security, privacy, and AI safety, and ensure compliance. The rise of AI agents is shifting the team into an "orchestration era," making governance a foundational architecture, not just a gate review.

Requirements

3+ years experience in a compliance, governance, program management, or technical analyst role in a high-scale tech environment.
Demonstrated ability to translate complex regulations (GDPR, CCPA, etc.) into implementable process controls and system requirements.
Experience designing and operationalizing governance, compliance, or risk management workflows for technical teams.
Strong cross-functional communication and stakeholder management skills.
Comfort operating with ambiguity and driving initiatives without formal authority.
Technical literacy: Ability to understand distributed systems (Kafka, Cassandra, Redis) and modern software development/CI/CD pipelines to design integrated controls.

Nice To Haves

Direct experience implementing governance controls for AI/ML pipelines or LLM-based product features.
Familiarity with orchestration frameworks, AI agent architectures, or tool-use systems (e.g., LangChain).
Experience with Policy-as-Code systems or defining requirements for automated compliance scanning.
Prior exposure to SDLC governance integration, threat modeling, or privacy impact assessments in a technical capacity.
Basic coding/scripting skills (e.g., Python, Go) sufficient to understand code and data flows.

Responsibilities

Translates regulatory and policy requirements into actionable governance frameworks for AI orchestration systems.
Designs and implements process guardrails and controls upstream—within AI orchestration prompts, system architecture, and LLM pipeline design—to ensure continuous governance.
Partners with engineering and legal teams to define, monitor, and audit the behavior of AI agents operating at scale.
Develops new governance workflows and documentation standards to manage the novel risks of AI-generated code, model behavior, and automated data flows.
Audits our AI landscape surface, including AI-generated code, model behavior, and data flow through automated pipelines.
Analyzes risk that comes from AI agents operating between human oversight moments, occurring faster and at greater volume.
Achieves compliance with external frameworks through Governance-as-Architecture, Model Output Monitoring, Constraint Layer Auditing, and Policy Implementation Workflow.
Develop governance standards and policy enforcement for AI-powered features and internal orchestration systems.
Design and implement workflows for defining, reviewing, and tracking governance constraints at the prompt, pipeline, and infrastructure level.
Define and implement audit surfaces for AI-generated code and model behavior to ensure accountability.
Develop monitoring and reporting frameworks for data flows through automated pipelines and LLM tool-use chains.
Own the governance lifecycle for new AI features, from risk scoping to operational monitoring.
Lead sessions with engineering, product, legal, and compliance to assess risk and design scalable solutions.
Act as a consultant, translating complex legal/policy requirements into concrete process steps and architectural needs.
Clearly communicate complex AI and governance risk to audiences ranging from engineers to executives.
Socialize AI governance best practices, building a culture where compliance is an architectural feature.
Work with Security and Privacy Engineers to ensure policy-as-code systems reflect governance requirements.
Contribute to the design of data lifecycle management (deletion, retention, access) processes for AI-driven data.
Develop and maintain key governance documentation, including tailored AI governance policies and risk assessment templates.