Data Engineer

Looking for W2 candidates who are in Las Vegas or Carson City, NV.

This will be 100% onsite.

NO C2C resume pls.

• Minimum of five years of relevant experience in enterprise data architecture, data modeling, data warehousing, Lakehouse solutions, or modern data platform design and implementation.

• Experience designing and implementing conceptual, logical, and physical data models in enterprise environments.

• Experience translating business requirements into scalable data architecture solutions.

• Experience designing data integration solutions and working with enterprise databases, data warehouses, or modern cloud-based data platforms.

• 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.

• Define and maintain source-to-consumer data contracts covering schemas, keys, data freshness, quality expectations, governance standards, compatibility, and change notification.

• Recommend platform choices for storage, processing, modeling, and analytics, including evaluation of database technologies, storage optimization, and emerging data platform capabilities.

• Identify opportunities to simplify existing data architecture, promote reusable data services and integration patterns, and optimize platform costs while maintaining scalability and performance.