AIML Data & ETL Data Architect

AI/ML Enablement & GenAI

• Architect feature stores training/inference pipelines and MLOps workflows for insurance use cases fraud detection claims triage underwriting risk scoring loss reserving and customer churn/retention.
  
• Design RAG and GenAI solution patterns for claims summarization policy/document intelligence and underwriter/agent copilots.
  
• Establish model lifecycle controls: versioning lineage drift monitoring evaluation and human-in-the-loop review.
  
• Define responsible-AI and governance guardrails appropriate to a regulated insurance environment (auditability explainability bias monitoring).
  

Data Architecture & Platform Design

• Own the end-to-end target-state architecture for the insurance data platform policy administration claims billing underwriting actuarial and reinsurance domains across raw curated and analytics-ready layers.
  
• Design lakehouse and AI/ML reference architectures (Bronze/Silver/Gold Medallion) that unify structured semi-structured and streaming insurance data.
  
• Define data domain boundaries source-to-target mappings and canonical insurance data models for shared enterprise consumption.
  
• Produce architecture diagrams design decision records and patterns that engineering teams can implement consistently.
  
• Make build-vs-buy cloud service selection and cost/performance trade-off decisions and defend them to client architecture review boards.
  

Data Engineering & ETL/ELT

• Design scalable production-grade ETL/ELT frameworks (PySpark Spark SQL Delta Live Tables / equivalent orchestrated Workflows).
  
• Define ingestion patterns for batch micro-batch and streaming insurance feeds (policy claims payments third-party/bureau data).
  
• Establish orchestration monitoring alerting and automation standards for the engineering team.
  

Data Modeling

• Design dimensional models (star/snowflake) and canonical/conformed models for analytical and actuarial workloads.
  
• Apply normalization/denormalization strategies balancing performance usability and regulatory traceability.
  
• Ensure data quality integrity and alignment with enterprise and insurance regulatory governance policies.
  

Governance Security & Compliance

• Embed PII/PHI handling masking tokenization and least-privilege access models into platform design.
  
• Align architecture with insurance regulatory and audit requirements (e.g. NAIC model standards state DOI HIPAA where health lines apply SOC 2 GDPR/CCPA).
  
• Define metadata management data lineage and cataloging strategy (Unity Catalog or equivalent).
  

Technology Stack

• Advanced hands-on data engineering: Spark Delta Lake / lakehouse Workflows Unity Catalog (or cloud-native equivalents).
  
• AI/ML tooling: MLflow or equivalent feature stores model serving and GenAI/RAG frameworks (LangChain/LangGraph or similar).
  
• Strong SQL and Python programming with performance tuning skills.
  
• Cloud platform depth (AWS / Azure / GCP) including managed data and ML services.
  

Required Qualifications

• Hands-on AI/ML pipeline and MLOps experience including at least one production GenAI/RAG deployment.
  
• Strong command of Medallion architecture (Bronze/Silver/Gold) and modern data modeling for warehousing and analytics.
  
• Proficiency with PySpark SQL ETL/ELT frameworks and Delta Lake (or equivalent) optimization.
  
• Experience with CI/CD Git and job orchestration tooling.
  
• Insurance financial services or other regulated-industry delivery experience.
  
• Demonstrated ability to present and defend architecture to senior client and review-board stakeholders.
  

Preferred Skills

• Data governance metadata management and Unity Catalog (or equivalent) advanced features.
  
• Streaming technologies (Auto-Loader / Structured Streaming / Kafka / Event Hubs / Kinesis).
  
• Data security regulatory compliance and fine-grained access models.
  
• Cost optimization and performance tuning in cloud environments.
  
• Responsible-AI / model governance frameworks (e.g. NIST AI RMF).
  
• Tools such as Airflow Databricks Workflows dbt or similar.
  

• Data architecture leadership lakehouse / Medallion (Bronze/Silver/Gold) target-state design
  
• Strong Python (PySpark) and SQL programming with performance tuning
  
• Databricks (or equivalent) Spark Delta Lake Workflows Unity Catalog
  
• ETL/ELT framework design and data modeling (dimensional star/snowflake canonical)
  
• AI/ML pipelines MLOps plus at least one production GenAI/RAG deployment
  
• Cloud experience AWS Azure or GCP (managed data ML services)
  
• CI/CD Git job orchestration
  
• 12 years total; 3 years as architect/lead; regulated-industry delivery