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AI/ML Data & ETL Data Architect

icon building Company : Dataeconomy
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Job Description - 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.
  • 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.


Requirements

  • Data architecture leadership  lakehouse / Medallion (Bronze/Silver/Gold) target -state design
  • 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


Benefits

Standard full -time benefits

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