Director, Data Engineering

Responsibilities:

We are seeking a strategic and technically accomplished Director, Data Products Engineering to lead the architecture, engineering, and delivery of AI products, data products and scalable data solutions across our fintech and payment processing ecosystem.

This leader will 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.

The role requires a strong combination of strategic data solution architecture expertise, modern cloud data engineering leadership, and product-thinking. The ideal candidate will lead teams responsible for engineering high-quality AI/data products, designing scalable data architectures, and enabling reliable enterprise data consumption at scale.

The current ecosystem includes:

About our Stack:

• Snowflake
• Dataiku
• dbt
• Fivetran
• Apache Iceberg on Amazon S3
• Looker & LookML
• SQL, Python
• AWS
• MySQL, PostgreSQL
• Gitlab
• Monte Carlo
• Terraform, OpenTofu
• RDS Database Insights and Datadog

This role will partner closely with Product, Engineering, Risk, and Operations teams to define enterprise data strategies, architect scalable data solutions, and operationalize high-value AI/data products that accelerate business growth and innovation.

Enterprise Data Product Leadership

• 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

Strategic Data Solution Architecture

• 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.

 Data Engineering Leadership

• 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. 

Team Leadership & Organizational Development

• 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.

Minimum Qualifications

• 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.

Preferred Qualifications

• 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.