Director, Engineer (ai, Data & Security Tooling Ecosystem)

Allstate Allstate · Insurance · United States · Remote

The Principal Engineer will be the technical authority for the AI, Security Operations Data, and security tooling ecosystem underpinning Cyber Operations. This role defines and enforces engineering standards for shared AI and data capabilities, ensuring reliability, security, observability, and auditability at enterprise scale. It also supports the integration of security-relevant tooling into the ecosystem with consistent patterns and automated controls.

What you'd actually do

  1. Ecosystem architecture and technical strategy: Define reference architecture, technical standards, and reusable primitives for AI enablement services, the Security Operations Data Plane, and ecosystem integrations.
  2. Engineering excellence and delivery discipline: Establish standards for code quality, test-driven development, availability, and secure software engineering practices.
  3. Agentic safety and governance-by-design: Engineer guardrails for AI-assisted and agentic workflows (bounded actions, least privilege, evidence logging, observability, auditability) and ensure secure-by-default configurations.
  4. Security Operations Data Plane engineering: Ensure ingestion, transformation, schema discipline, enrichment, and analytics readiness are engineered for reliability, performance, and defensible operations.
  5. Tooling readiness and lifecycle stewardship: When tooling operation is required, define integration contracts (APIs, schemas, telemetry, access controls), automate configuration, and ensure tooling adheres to ecosystem standards and monitoring.

Skills

Required

  • 7–10 years of relevant new product or technology development experience
  • shipping software
  • managing complex integrations
  • lead cross-product technical strategy
  • influence senior stakeholders
  • CI/CD practices
  • operating production systems with accountability to KPIs/outcomes

Nice to have

  • Security data and analytics engineering
  • log/telemetry pipelines
  • schema governance
  • normalization/enrichment
  • query-driven analytics concepts
  • cloud-native logging/analytics platforms
  • time-series/log query languages
  • data ingestion/transformation patterns
  • Automation and platform engineering
  • reusable libraries/services
  • developer enablement through paved roads/golden paths
  • Agentic AI secure-by-design practices
  • least-privilege tool access
  • restricted interactions
  • resilience to manipulation
  • evidence logging / explainability expectations
  • Risk, privacy, and compliance alignment
  • partnering with risk/legal/privacy stakeholders
  • assessment processes for AI-enabled capabilities
  • regulated data handling

What the JD emphasized

  • AI-enabled threat landscape
  • AI-first principles
  • AI enablement services
  • Agentic AI secure-by-design practices
  • security-relevant tooling

Other signals

  • AI-enabled threat landscape
  • AI-first principles
  • AI enablement services
  • Agentic AI secure-by-design practices