Data Warehouse Engineer

Job Title: Data Warehouse Engineer



Location: Remote
Client: California Department of Motor Vehicles

Responsibilities

• Analyze source system structures, extract requirements, and transformation logic needed to support business reporting and analytics.
• Review data validation, reconciliation, and error-handling routines to assess data reliability and transparency.
• Observe procedures around the design, build, test, and maintenance processes for ETL workflows, batch jobs, and data integration routines supporting the enterprise data warehouse to evaluate proper handling and execution of M&O responsibilities by T/T vendors.
• Support incident resolution, root cause analysis, and corrective actions for failed or degraded data loads.
• Review documentation of ETL designs, source-to-target mappings, transformation logic, and operational procedures for completeness, accuracy, and clarity.
• Collaborate with team to evaluation data platform alignment with compliance requirements, business rules, and reporting needs.
• Support change management activities related to new interfaces, source changes, legislative updates, and production releases.

Qualifications

• 8+ years of experience developing ETL workflows for production-level data warehouse environments (i.e., multiple experiences with different EDWs).
• Hands-on experience working with Teradata and other types of SQL databases.
• Exceptional communication skills, both written and verbal.
• Exceptional attention to detail and high-quality delivery.
• Consultative mindset, with ability to teach and lead multi-skilled teams of both technical and functional resources.

Sample Tasks

Review data feeds, processing jobs, error logs and resolution procedures, and any other data transformations involved with DW maintenance and operations to understand how service continuity is sustained and critical functions required to prevent disruption during vendor transition (with the expertise to recognize, communicate, and provide recommendations around gaps and risks arising during transition activities).

• Implement data quality checks for completeness, referential integrity, duplicate detection, and business-rule validation.
• Monitor daily, weekly, or monthly data loads and troubleshoot failed jobs, data anomalies, or performance bottlenecks.
• Review processes that support deployment activities, including unit testing, integration testing, and promotion of ETL code across environments to assess quality control and risk management.
• Contribute to technical documentation and support materials for production operations and knowledge transfer.