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.

Job Title

Principal, Data & AI Platform Engineer

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 FinTech 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 toolchains.
• 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 FinTech 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 architectures

Cloud & Platforms

• Experience with Snowflake in enterprise environments
• Hands‑on exposure to cloud‑native or private cloud platforms (AWS, on‑prem, or hybrid