Be responsible for the technology strategy and accountable for driving delivery. You will author the technical vision and lead the development of the roadmap for the Personalization Platform.
Partner with the team leader, product manager, data science and other key stakeholders to identify and deliver technical solutions that can solve common problems across the enterprise.
Be a thought partner in relentlessly improving our ways of working and technical excellence, to increase cross-functional collaboration, test & learn velocity, throughput, autonomy, innovation, talent development.
Ensure the right balance between short term value creation and long-term platform investments.
Bring your technical excellence and expertise in distributed system architecture to build scalable, performant, extensible, and reusable, services, APIs, and systems that enable personalized experiences throughout the organization at scale
Be a technical role model guiding and inspiring technical craftmanship, innovation and adoption of the latest engineering best practices
Build and empower an outcome oriented, collaborative, trust-based, and inclusive team of world-class machine learning, data and full-stack engineers that fosters a culture of ownership, experimentation, innovation, and technical excellence
Champion agile development and modern product management best practices as a mean to build the right things in the right way.
5+ years in a leadership/management role with experience building engineering teams. Experience in recommendations and search engines is a huge plus (e.g., personalization at scale, Elasticsearch, performance benchmarks, etc).
10+ years of progressing experience in software/data engineering, ideating and architecting distributed, scalable, enterprise-class systems. Ability to dive deep into system architecture, design, performance metrics, code, test plans, project plans, deployments, and operations.
Proficiency in the entire machine learning lifecycle implementation and management (i.e., ML Model Development and Deployment Frameworks, Data Architecture, ELT/ETL tools & concepts, Data Integration, Data Catalog and Query, Data Wrangling, Data Quality, etc.) and experience working with Data scientists.
Proficiency in distributed computing, micro-services architecture, caching frameworks (eg: Redis), data structures, algorithms, operating systems, NoSQL data stores (eg: MongoDB), databases and server-side technologies with an eye towards scale (PostgreSQL or MySQL), automation, resiliency, high availability, and user experience. Experience with Java/J2EE, RESTful APIs, event driven architecture, cloud computing (Google Cloud, AWS), integration patterns and machine learning stack (e.g., Airflow/Kuberflow, Dockers, Kubernetes etc.)
Experience with continuous integration and continuous development solutions.
Familiarity with A/B experimentation and data/metric-driven product development.
Ability to lead through influence across different groups and levels in the organization.
Excellent interpersonal, communication, and collaboration skills.
Master’s degree in Computer Science, Information Technology, Software Engineering, Electrical Engineering, or equivalent experience.