AI/ML Data & ETL Data Architect

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).

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.