AI​/ML Data & ETL Data Architect

Charlotte, United States | Posted on 06/04/2026

DATAECONOMY is one of the fastest-growing Data & Analytics company with global presence. We are well-differentiated and are known for our Thought leadership, out-of-the-box products, cutting-edge solutions, accelerators, innovative use cases, and cost-effective service offerings.

We offer products and solutions in Cloud, Data Engineering, Data Governance, AI/ML, Dev Ops and Blockchain to large corporates across the globe. Strategic Partners with AWS, Collibra, cloudera, neo4j, Data Robot, Global IDs, tableau, Mule Soft and Talend.

AI/ML Data & ETL Data Architect

Charlotte, NC

Full-time

Key Responsibilities

• 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).
• 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.
• 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.

• 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.

• 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).
• 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 (Lang Chain/Lang Graph or similar).
• Strong SQL and Python programming with performance tuning skills.
• Cloud platform depth (AWS / Azure / GCP), including managed data and ML services.

• 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.
• P…