Principal, Data & AI Platform Engineer

Principal, Data & AI Platform Engineer

Calling all innovators - find your future at Fiserv.

We're Fiserv, a global leader in Fintech and payments, and we move money and information in a way that moves the world. We connect financial institutions, corporations, merchants and consumers to one another millions of times a day - quickly, reliably, and securely. Any time you swipe your credit card, pay through a mobile app, or withdraw money from the bank, we're involved.

If you want to make an impact on a global scale, come make a difference at Fiserv.

About the Role

Design, build, and operate a secure, on-premise analytics and AI platform that unifies transactional data from PostgreSQL, DynamoDB, and other source databases into Snowflake, and applies machine learning, LLMs, and advanced analytics to generate business-critical reports, insights, and operational efficiencies.

This role owns end-to-end technical delivery—from data ingestion and modeling to AI-driven analytics—while ensuring strict data security, governance, and compliance suitable for highly regulated Fin Tech environments. Public AI services are not permitted; all AI/ML workloads must run on-prem or in private infrastructure.

What You'll Do

Data Platform & Snowflake Engineering

• Design and implement secure data pipelines to migrate and unify data from PostgreSQL, DynamoDB, and other source databases into Snowflake.
• Build and optimize ELT/ETL workflows, data models, and schemas in Snowflake for analytics and AI use cases.
• Own Snowflake performance tuning, cost optimization, clustering, and secure data sharing patterns.
• Ensure high data quality, lineage, and reconciliation between source systems and Snowflake.

Analytics & Reporting

• Build analytics datasets and semantic layers to support enterprise reporting, dashboards, and ad-hoc analysis.
• Enable self-service analytics for business and operations teams using governed datasets.
• Collaborate with product and business stakeholders to define KPIs, metrics, and reporting logic.

Machine Learning & LLM Enablement (On-Prem)

• Design and deploy on-prem ML and LLM solutions for reporting automation, anomaly detection, forecasting, and operational insights.
• Implement private / self-hosted LLM architectures (e.g., containerized or VM-based) with secure inference pipelines.
• Develop ML pipelines for feature engineering, training, validation, and inference using enterprise-approved tool chains.
• Integrate AI outputs into applications, workflows, and reporting solutions.

Operational Efficiency via AI

• Implement AI-driven automations for operational efficiencies such as:
• Automated report generation and narrative insights
• Data anomaly detection and monitoring
• Intelligent alerting and triage
• Workflow optimization and decision support
• Measure and continuously improve AI model accuracy, performance, and business impact.

Application & API Integration

• Expose secure APIs and services for data access, analytics, and AI inference.
• Integrate analytics and AI capabilities with existing Java / Spring Boot-based services and applications.
• Follow secure API practices, including authentication, authorization, and token-based access.

Security, Compliance & Governance

• Enforce data security, encryption, access controls, and governance across PostgreSQL, Snowflake, and AI platforms.
• Ensure sensitive Fin Tech data never leaves approved infrastructure or flows into public AI models.
• Work closely with security teams to support audits, compliance, and risk remediation.
• Apply secure coding practices and address findings from SCA and security scanning tools.

What you will need

Data & Analytics

• Strong SQL expertise with PostgreSQL and Snowflake, data modeling, performance tuning, and optimization
• ETL/ELT frameworks and data orchestration tools

AI / ML

• Hands-on experience with machine learning pipelines and analytics-driven ML use cases
• Experience working with LLMs in private or on-prem environments
• Understanding of prompt engineering, embeddings, vector search, and inference optimization
• Python for ML, data processing, and analytics

Application Development

• Experience integrating analytics and AI into enterprise applications
• Knowledge of microservices and API-driven…