AI/ML Data & ETL Data Architect

AI/ML Enablement & GenAI<\/span>
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• 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.<\/span>
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• Design RAG and GenAI solution patterns for claims summarization, policy/document intelligence, and underwriter/agent copilots.<\/span>
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• Establish model lifecycle controls: versioning, lineage, drift monitoring, evaluation, and human -in -the -loop review.<\/span>
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• Define responsible -AI and governance guardrails appropriate to a regulated insurance environment (auditability, explainability, bias monitoring).<\/span>
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  Data Architecture & Platform Design<\/span>
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  • 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.<\/span>
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  • Design lakehouse and AI/ML reference architectures (Bronze/Silver/Gold Medallion) that unify structured, semi -structured, and streaming insurance data.<\/span>
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  • Define data domain boundaries, source -to -target mappings, and canonical insurance data models for shared enterprise consumption.<\/span>
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  • Produce architecture diagrams, design decision records, and patterns that engineering teams can implement consistently.<\/span>
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  • Make build -vs -buy, cloud service selection, and cost/performance trade -off decisions and defend them to client architecture review boards.<\/span>
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    Data Engineering & ETL/ELT<\/span>
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    • Design scalable, production -grade ETL/ELT frameworks (PySpark, Spark SQL, Delta Live Tables / equivalent, orchestrated Workflows).<\/span>
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    • Define ingestion patterns for batch, micro -batch, and streaming insurance feeds (policy, claims, payments, third -party/bureau data).<\/span>
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    • Establish orchestration, monitoring, alerting, and automation standards for the engineering team.<\/span>
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      Data Modeling<\/span>
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      • Design dimensional models (star/snowflake) and canonical/conformed models for analytical and actuarial workloads.<\/span>
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      • Apply normalization/denormalization strategies balancing performance, usability, and regulatory traceability.<\/span>
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      • Ensure data quality, integrity, and alignment with enterprise and insurance regulatory governance policies.<\/span>
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        Governance, Security & Compliance<\/span>
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        • Embed PII/PHI handling, masking, tokenization, and least -privilege access models into platform design.<\/span>
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        • Align architecture with insurance regulatory and audit requirements (e.g., NAIC model standards, state DOI, HIPAA where health lines apply, SOC 2, GDPR/CCPA).<\/span>
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        • Define metadata management, data lineage, and cataloging strategy (Unity Catalog or equivalent).<\/span>
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          Technology Stack<\/span>
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          • Advanced hands -on data engineering: Spark, Delta Lake / lakehouse, Workflows, Unity Catalog (or cloud -native equivalents).<\/span>
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          • AI/ML tooling: MLflow or equivalent, feature stores, model serving, and GenAI/RAG frameworks (LangChain/LangGraph or similar).<\/span>
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          • Strong SQL and Python programming with performance tuning skills.<\/span>
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          • Cloud platform depth (AWS / Azure / GCP), including managed data and ML services.<\/span>
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            Required Qualifications<\/span>
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            • Hands -on AI/ML pipeline and MLOps experience, including at least one production GenAI/RAG deployment.<\/span>
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            • Strong command of Medallion architecture (Bronze/Silver/Gold) and modern data modeling for warehousing and analytics.<\/span>
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            • Proficiency with PySpark, SQL, ETL/ELT frameworks, and Delta Lake (or equivalent) optimization.<\/span>
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            • Experience with CI/CD, Git, and job orchestration tooling.<\/span>
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            • Insurance, financial services, or other regulated -industry delivery experience.<\/span>
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            • Demonstrated ability to present and defend architecture to senior client and review -board stakeholders.<\/span>
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              Preferred Skills<\/span>
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              • Data governance, metadata management, and Unity Catalog (or equivalent) advanced features.<\/span>
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              • Streaming technologies (Auto -Loader / Structured Streaming / Kafka / Event Hubs / Kinesis).<\/span>
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              • Data security, regulatory compliance, and fine -grained access models.<\/span>
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              • Cost optimization and performance tuning in cloud environments.<\/span>
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              • Responsible -AI / model governance frameworks (e.g., NIST AI RMF).<\/span>
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              • Tools such as Airflow, Databricks Workflows, dbt, or similar.<\/span>
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                Requirements<\/h3>

                • Data architecture leadership  lakehouse / Medallion (Bronze/Silver/Gold) target -state design<\/span>
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                • Strong Python (PySpark) and SQL programming with performance tuning<\/span>
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                • Databricks (or equivalent) Spark, Delta Lake, Workflows, Unity Catalog<\/span>
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                • ETL/ELT framework design and data modeling (dimensional, star/snowflake, canonical)<\/span>
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                • AI/ML pipelines + MLOps, plus at least one production GenAI/RAG deployment<\/span>
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                • Cloud experience AWS, Azure, or GCP (managed data + ML services)<\/span>
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                • CI/CD, Git, job orchestration<\/span>
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                • 12+ years total; 3+ years as architect/lead; regulated -industry delivery<\/span><\/span>
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                  Benefits<\/h3>

                  Standard full -time benefits<\/div><\/span>