PayNearMe
Director, Data Product Engineering
RemoteUnited States only
- Role
- Data Engineering
- Experience
- C-Level
- Employment
- Full-time
- Company size
- Mid-size
$200k–$245k/yr
Check eligibility
Open to US only. Set where you work from to check your eligibility.
No BS summary
Seeking a Director of Data Product Engineering to lead AI and data product architecture, engineering, and delivery. Requires strong strategic data solution architecture, modern cloud data engineering leadership, and product-thinking. Must build trusted, reusable, scalable AI/data products for analytics, AI/ML, and decision-making.
Core skills
AI productsdata productsscalable data solutions
Required skills
SnowflakeDataikudbtFivetranApache IcebergLookerLookMLSQLPythonAWSMySQLPostgreSQLGitLabMonte CarloOpenTofuTerraformRDSDatadogpredictive analyticsmachine learningrecommendation systemsdecision intelligenceAI-enabled analytics
Optional skills
real-time analyticsstreaming architecturesMLOps platformsFeature storesVector databasesSemantic retrieval architecturesAgentic AI frameworksPCI
What you'll do
- 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.
- 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.
- 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 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.
What they require
- 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.
Benefits
- Competitive salary and benefits with growth-company options grant
- Fast- paced and professional work culture
- Stock options with standard startup vesting - 1 year cliff; 4 years total
- $50 monthly communication expense stipend to go towards your phone/internet bill
- $250 stipend to enhance your WFH setup
- Reimbursement for peripheral equipment: monitor (up to $400), keyboard and mouse (up to $200)
- Premium medical benefits including vision and dental (100% coverage for employees)
- Company-sponsored life and disability insurance
- Paid parental bonding leave
- Paid sick leave, jury duty, bereavement
- 401k plan
- Flexible Time Off (our team members typically take off ~3-4 weeks per year)
- Volunteer Time Off
- 13 scheduled holidays
PayNearMe builds payment technology and its PayXM platform manages payment experiences for non-commerce businesses across cards, ACH, digital wallets and cash retail locations.
FintechMid-size
$200k–$245k/yr