AI/ML Data & ETL Data Architect

Job Information

• Date Opened: 06/04/2026
• Job Type: Full time
• Industry: IT Services
• Location: Charlotte, North Carolina 28202, United States

About DATAECONOMY

DATAECONOMY is one of the fastest-growing Data & Analytics companies with global presence. We are well-differentiated and known for 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.

Position

AI/ML Data & ETL Data Architect

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)

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)

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
• 12+ years total experience; 3+ years as architect/lead; regulated-industry delivery

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

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

Benefits

Standard full-time benefits