Director, Data Product Engineering

PayNearMe is on a mission to simplify payments through innovative technology. They are seeking a Director of Data Product Engineering to lead the development of AI and data products that enhance their payment processing ecosystem.

Responsibilities:

• Lead the architecture, engineering, and delivery of AI products, data products and scalable data solutions across our fintech and payment processing ecosystem
• Drive the company’s transition toward a product-centric data operating model by building trusted, reusable, scalable, and business-aligned AI/data products that power analytics, operational intelligence, AI/ML initiatives, customer experiences, regulatory reporting, and enterprise decision-making
• Lead teams responsible for engineering high-quality AI/data products, designing scalable data architectures, and enabling reliable enterprise data consumption at scale
• Collaborate with the Data leadership team on the refinement of our strategy for Data Products Engineering and scalable data product delivery with a focus on enabling/building AI-powered solutions
• Establish a product-centric operating model for data capabilities, emphasizing: Reusable and governed data products with a focus on accelerating AI/data products, Domain-oriented ownership, Data contracts and SLAs, Product lifecycle management, Discoverability and interoperability, Standardized business metrics and semantic models
• Partner with business and technology stakeholders to identify, prioritize, and deliver strategic data products aligned to enterprise goals
• Drive the creation of scalable enterprise data assets supporting: Fraud and risk intelligence, Transaction analytics, Merchant and customer insights, Financial and operational reporting, AI/ML enablement, Regulatory and compliance requirements
• Lead strategic architecture and engineering decisions for domain data solutions and our modern cloud-based analytical AI/data platform expansion
• Design scalable, resilient, and AI-ready data architectures that support high-volume transactional processing and analytical workloads
• Collaborate with Data team leadership on enterprise standards for: Data modeling and semantic design, ELT/ETL frameworks, Data orchestration, Data quality and observability, Metadata management and lineage, Data governance and security, Performance optimization and scalability
• Architect data solutions that enable trusted, near real-time, and self-service access to enterprise data
• Drive architectural alignment across operational systems, analytics platforms, AI/ML environments, and reporting ecosystems
• Partner with Architecture, Cloud Engineering, and Security teams to ensure long-term AI and data product scalability, interoperability, and compliance
• Lead and scale high-performing Data Product Engineering team responsible for domain AI product and data product delivery
• Oversee development and operationalization of scalable cloud-native data pipelines and data services
• Drive modernization of legacy data workflows and platforms to improve agility, scalability, and operational efficiency
• Ensure data products are optimized for analytics, predictive modeling, and AI/ML consumption
• Build, mentor, and develop a high-performing teams
• Foster a culture of engineering excellence, ownership, innovation, and continuous improvement
• Promote modern engineering and architectural practices across the organization
• Establish career frameworks, mentorship programs, and capability development strategies for technical teams
• Lead strategic vendor and technology partner relationships supporting data engineering and platform initiatives

Requirements:

• Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, Information Systems, or related field required; Master's or PhD preferred
• 10+ years of progressive leadership experience in Data, Analytics, or AI/ML organizations
• 5+ years leading enterprise-scale analytics, data science, or AI engineering teams
• Strong hands-on expertise in predictive analytics, machine learning, recommendation systems, decision intelligence, and AI-enabled analytics
• Proven experience building scalable enterprise data products
• Deep experience with modern cloud data platforms and analytical ecosystems including: Snowflake, Dataiku, dbt, Fivetran, Apache Iceberg, Looker / LookML
• Strong technical expertise in: Python, SQL, ML frameworks and AI tooling, Cloud platforms such as AWS
• Strong executive communication and stakeholder management skills
• Experience leading within complex, matrixed organizations
• Exceptional communication and stakeholder management skills with ability to influence executive and technical audiences
• Experience within fintech, payment processing, transaction platforms, fraud analytics, or regulated financial services
• Experience with real-time analytics and streaming architectures
• Familiarity with: MLOps platforms, Feature stores, Vector databases, Semantic retrieval architectures, Agentic AI frameworks
• Knowledge of PCI, SOC2, GDPR, and financial data governance requirements
• Experience integrating predictive AI and analytical AI capabilities with broader GenAI enterprise initiatives