Associate Director, Data and Analytics

Own the end-to-end technical design and delivery of Decision Hub capabilities (eligibility, arbitration, offering services, monitoring, APIs, etc.). Champion and lead the adoption of AI-assisted development practices (e.g., GitHub Copilot, generative AI for code/test generation) to accelerate delivery, improve code quality, and foster a culture of innovation. Spearhead the integration of Generative AI, adaptive models, and other emerging AI technologies into the platform's core capabilities, moving beyond traditional ML models to create a truly intelligent system. Collaborate with Product, Solution Architecture, Data, and Security to ensure compliance with regulatory controls, data lineage, and auditability. Drive performance, scalability, and resilience design; validate via capacity/performance testing and production dry runs. Translate business requirements into extensible, maintainable technical solutions and reference designs for multi-market reuse. Lead technical design and code reviews, setting the standard for high-quality, efficient, and maintainable code. Hands-on implementation and troubleshooting; unblock teams during critical incidents and deployment windows. Coach and grow engineering capability; set engineering practices, quality metrics, and a high-performance culture. 8+ years in software engineering with 3+ years in technical lead or architect roles on distributed, real-time systems. Proven experience designing and delivering decisioning, orchestration, or real-time personalization platforms (or equivalent large-scale event/streaming systems). Strong cloud design and development experience (GCP preferred; AWS/Azure acceptable) and familiarity with multi-cloud/on-prem tradeoffs. Hands-on experience with the end-to-end machine learning lifecycle (MLOps), from model integration and deployment to performance monitoring and feedback loops. A strong passion for and practical experience with leveraging AI development tools (e.g., GitHub Copilot, CodeWhisperer) and embedding them into team workflows. Deep knowledge of performance engineering, capacity planning, fault tolerance, and observability (APM, metrics, tracing, alerting). Hands-on with modern engineering practices: microservices, APIs, CI/CD, infra as code, automated testing, security controls. Excellent stakeholder skills: ability to translate complex technical concepts for business partners and influence product and delivery decisions. Strong mentoring and team leadership track record.