AI/ML Data & ETL Data Architect

DATAECONOMY Inc; Charlotte (NC); On-site; USD 130,000 - 180,000; Full time

Job summary

DATAECONOMY Inc is seeking an AI/ML Data & ETL Data Architect in Charlotte, NC. This full-time role is crucial for architecting solutions for AI/ML pipelines, feature stores, and MLOps workflows primarily for the insurance domain.

Key responsibilities include designing data models and establishing governance controls. The ideal candidate will have extensive experience in cloud platforms and data engineering. Join us in shaping innovative data solutions and strategies.

Qualifications

• Hands-on AI/ML pipeline experience including production deployments.
• Strong command of modern data modeling for warehousing.
• Experience with CI/CD and job orchestration tools.

Responsibilities

• Architecting feature stores and MLOps workflows.
• Designing RAG and GenAI solutions for insurance.
• Establishing model lifecycle controls and governance.

Skills

AI/ML pipeline and MLOps experience

Medallion architecture expertise

Proficiency with PySpark

Strong SQL and Python skills

Cloud platform depth (AWS/Azure/GCP)

Education

12+ years total experience

Tools

Delta Lake

Git

Databricks

Job description

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, DevOps and Blockchain to large corporates across the globe. Strategic Partners with AWS, Collibra, cloudera, neo4j, DataRobot, Global IDs, tableau, MuleSoft and Talend.

AI/ML Data & ETL Data Architect

Charlotte, NC

Full-time

Key Responsibilities

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).
• 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.

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).
• 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.
• Tools such as Airflow, Databricks Workflows, dbt, or similar.

Requirements

• 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