About the job
The Director of Engineering – Decision Intelligence Platform will lead the strategy, architecture, build, and scaling of an AI/ML-driven Decision Intelligence platform that powers enterprise decisioning (Next Best Action/Next Best Offer), journey management, and real-time personalization across digital and assisted channels. This leader will be accountable for establishing a robust data-and-model foundation (streaming + batch), a reusable decisioning layer, and low-latency activation services that translate customer signals into context-aware experiences in milliseconds.
Responsibilities
Set vision and roadmap for an AI/ML-driven Decision Intelligence platform enabling NBA/NBO, journey orchestration, and real-time personalization across channels.
Partner with Marketing, Product, Analytics, and Business leaders to prioritize high-impact use cases and deliver measurable outcomes (uplift, conversion, churn, CLV, cost-to-serve).
Define the reference architecture across event streaming, identity, feature store, MLOps, decision services, and activation—optimized for low latency, scale, governance, and explainability.
Evaluate and adopt new capabilities (real-time inference, bandits/experimentation, privacy-enhancing techniques) based on business value and operational readiness.
Build and lead a high-performing org across software, data, ML, and analytics; drive a platform culture of reusable components, productized APIs, self-service tooling, and operational excellence.
Drive execution via clear OKRs, prioritization, dependency management, and cross-functional operating rhythms with MarTech, IT, Governance, Security, and channel teams.
Deliver core platform capabilities: batch/streaming pipelines, real-time signals ingestion, identity/profile, feature pipelines (online/offline parity), model serving and monitoring (drift), decision engine (models + rules + constraints), and orchestration/activation integrations.
Qualifications
Minimum
15+ years in technology, including 8+ years leading cross-functional teams across engineering, data, and ML for customer-facing platforms.
10+ years building cloud-scale data platforms (lake/warehouse, ETL/ELT, streaming).
Proven integration with MarTech/CDP/journey orchestration and activation ecosystems (Adobe/Salesforce/Marketo/HubSpot or similar), focused on real-time activation and personalization.
End-to-end ownership of platform architecture through production operations, including governance and lifecycle management.
Strong record of building high-performing teams (including managers of managers) and aligning business and technical stakeholders.
Product/platform mindset with disciplined prioritization to drive adoption and measurable customer outcomes.
Clear communicator who can explain NBA/NBO, experimentation, and model risk to non-technical audiences.
Ability to manage competing priorities while maintaining quality, security, and reliability.
Expertise in cloud-scale distributed systems and low-latency, high-throughput real-time decisioning (API-first, event-driven architectures; Azure/AWS/GCP).
Hands-on experience with key stack components: Adobe Experience Platform (AEP) (RT-CDP, AJO, Target, CJA); Streaming (Kafka, Flink, Spark or equivalent); Data platforms (Snowflake/lakehouse), strong SQL and NoSQL; Microservices (Java/Node), Kubernetes, API gateway/service mesh patterns; Observability & reliability (logs/metrics/traces, alerting, SLO/SLA management); Deep understanding of Decision Intelligence patterns: NBA/NBO, orchestration, eligibility/constraints, frequency capping, and omnichannel policy consistency; Proven production ML delivery: feature engineering, feature store parity, model building, model serving, MLOps, monitoring/drift, retraining, and experimentation (A/B, uplift; bandits where appropriate); Strong modern engineering practices: Agile/SAFe, DevOps/MLOps, CI/CD, IaC, secure-by-design, and automated testing for data/ML/decision logic; Experience using AI coding assistants to accelerate delivery while maintaining code quality and ownership; Ability to build measurement and reporting for decision performance (KPIs, funnels, offer/model performance, closed-loop feedback).
Preferred
Strong record of building high-performing teams (including managers of managers) and aligning business and technical stakeholders.
Product/platform mindset with disciplined prioritization to drive adoption and measurable customer outcomes.
Clear communicator who can explain NBA/NBO, experimentation, and model risk to non-technical audiences.
Ability to manage competing priorities while maintaining quality, security, and reliability.