What are the responsibilities and job description for the Azure Data Architect position at KBTS Technologies Inc?
DIRECT CLIENT:
Data Architect
12 months
100% onsite in Las Vegas OR Carson City, NV
Core Responsibilities (Primary Architecture & Engineering Work)
• Extensive experience designing, evaluating, implementing, and optimizing Data and Analytics architecture and technologies.
• Added benefit: hands-on experience with Azure-based Lakehouse components (ADLS, ADF, Databricks, Snowflake, Synapse, MS Fabric).
• General understanding of networking, server, identity, and security architectures as they relate to data architecture (e.g., how data platforms interact with networks, compute, and security patterns). Define and validate appropriate data access and protection controls across ingestion, storage, transformation, reporting, and data export layers in collaboration with security and platform teams.
• Define conceptual, logical, and physical data models for NDOT, including reporting data models, shared measures, consistent business definitions, historical reporting requirements, and authorized data consumption patterns in collaboration with reporting owners.
• Translate business requirements into appropriate data models, metadata specifications, and data quality requirements.
• Create and maintain enterprise data and analytics architecture diagrams and roadmaps for NDOT.
• Map current-state and target-state data flows across systems and applications.
• Define and maintain end-to-end data lineage across ingestion, transformation, and consumption layers.
• Maintain source inventory, ownership, dependencies, and transition plans.
• Architect, document, and oversee ETL/ELT pipelines and multi-stage transformations.
• Implement automated CI/CD pipelines to ensure reliable and consistent delivery of data platform components.
• Design ingestion, staging, curation, and serving layers within Lakehouse environments.
• Ensure pipeline architectures support reliability, auditability, lineage tracking, and monitoring.
• Establish and enforce enterprise data standards, metadata definitions, and naming conventions.
• Define integration patterns for batch, streaming, event-driven, and API-based data flows.