AI/ML 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