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.

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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

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

• 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

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

• Experience with Snowflake in enterprise environments
• Hands-on exposure to cloud-native or private cloud platforms (AWS, on-prem, or hybrid)
• Containerization (Docker, Kubernetes) for AI/ML and analytics workloads

• Strong understanding of secure data handling, encryption, and access control
• Experience working in regulated environments (FinTech preferred)
• Familiarity with secure transactions and audit requirements

What You Will Need to Have (Minimum Qualifications)

• 8+ years of experience in software engineering, data platforms, or analytics engineering, owning production-grade systems end to end.
• Strong expertise in SQL, with hands-on experience in Snowflake and PostgreSQL, including data modeling, performance tuning, and optimization.
• Proven experience building and operating secure ETL/ELT data pipelines and analytics platforms at enterprise scale.
• Hands-on experience with machine learning and analytics-driven AI use cases (e.g., anomaly detection, forecasting, reporting automation).
• Experience working with LLMs in private or on-prem environments, including inference pipelines, embeddings, or vector search.
• Proficiency in Python for data processing, analytics, and ML workflows.
• Experience integrating analytics and AI capabilities into enterprise applications via APIs and services.
• Familiarity with microservices and REST APIs, including integration with Java / Spring Boot–based services.
• Experience deploying workloads in on-prem, private cloud, or hybrid environments, including containerized deployments (Docker/Kubernetes).
• Strong understanding of data security, encryption, access controls, and operating in regulated environments (financial services, FinTech, or similar).
• Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).

Preferred Qualifications

• Experience designing enterprise analytics platforms enabling governed, self-service reporting.
• Hands-on experience implementing AI-driven operational automation (automated insights, alerting, or decision support).
• Familiarity with Snowflake cost management, clustering strategies, or secure data sharing.
• Prior exposure to FinTech, payments, or transaction-heavy data domains.
• Experience collaborating with product, business, and security stakeholders on KPI definition and compliance-aligned analytics.
• Experience working in Agile development environments.

Salary Range

$110,000.00 - $186,000.00

These pay ranges apply to employees in New Jersey and New York. Pay ranges for employees in other states may differ.

It is unlawful to discriminate against a prospective employee due to the individual's status as a veteran.

For incentive eligible associates, the successful candidate is eligible for an annual incentive opportunity which may be delivered as a mix of cash bonus and equity awards in the Company's sole discretion.

Thank you for considering employment with Fiserv. Please:

• Apply using your legal name
• Complete the step-by-step profile and attach your resume (either is acceptable, both are preferable).

Our commitment to Equal Opportunity:

Fiserv is proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, gender identity, sexual orientation, age, disability, protected veteran status, or any other category protected by law.

If you have a disability and require a reasonable accommodation in completing a job application or otherwise participating in the overall hiring process, please contact View email address on click.appcast.io. Please note our AskHR representatives do not have visibility to your application status. Current associates who require a workplace accommodation should refer to Fiserv's Disability Accommodation Policy for additional information.

Note to agencies:

Fiserv does not accept resume submissions from agencies outside of existing agreements. Please do not send resumes to Fiserv