Principal, Data & AI Platform Engineer

Fiserv•Berkeley Heights, NJ

22h•Onsite

About The Position

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.

Requirements

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
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).

Nice To Haves

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.

Responsibilities

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.