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.
- 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).
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.
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.
- Responsible-AI / model governance frameworks (e.g., NIST AI RMF).
- Tools such as Airflow, Databricks Workflows, dbt, or similar.