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Principal, Data & AI Platform Engineer

Job in Berkeley Heights, Union County, New Jersey, 07922, USA
Listing for: SwiftCruit
Full Time position
Listed on 2026-07-07
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Data Engineering, AWS
Salary/Wage Range or Industry Benchmark: 110000 - 186000 USD Yearly USD 110000.00 186000.00 YEAR
Job Description & How to Apply Below

Principal, Data & AI Platform Engineer

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.

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. Measure and continuously improve AI model accuracy, performance, and business impact.
  • 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.
  • 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, governance across PostgreSQL, Snowflake, and AI platforms. Ensure sensitive Fin Tech data never leaves approved infrastructure or flows into public AI models. Support audits, compliance, risk remediation. Apply secure coding practices and address findings from SCA and security scanning tools.
Required

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 ELT/ETL 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 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, Fin Tech, or similar).
  • Bachelor’s degree…
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