Data Engineer

We are looking for an experienced Data Engineer to strengthen and scale an enterprise data environment in California. This contract opportunity with potential for a permanent role is ideal for someone who enjoys building reliable cloud-based data solutions, shaping reporting-ready datasets, and connecting information from diverse business platforms. The role offers meaningful ownership across data modeling, pipeline development, and analytics enablement while working closely with stakeholders in a collaborative setting.

Responsibilities:

• Create and refine scalable data structures that support reporting, analytics, and long-term platform growth.
• Develop and maintain curated datasets that are dependable, well-organized, and ready for business intelligence use.
• Connect data from multiple enterprise applications and align it into a consistent, usable format.
• Build and manage mappings, reference relationships, and cross-system data associations to improve data consistency.
• Enhance existing ingestion and transformation workflows to support expanding business and analytics needs.
• Strengthen platform stability through better monitoring, testing practices, and operational improvements.
• Tune and optimize data processing performance while contributing to ongoing platform enhancements.
• Produce clear documentation for lineage, transformation rules, and support procedures to improve maintainability.
• Collaborate with business partners to deliver high-confidence data assets that support reporting and decision-making.
• Help establish engineering standards, documentation practices, and technical guidelines across the data platform.
• At least 5 years of practical experience in data engineering roles.
• Minimum 3 years of hands-on Snowflake experience in production data environments.
• Strong knowledge of dimensional data modeling, including star schema design for reporting solutions.
• Solid programming ability in Python for data processing and platform development.
• Experience with code-driven transformation tools such as SQLMesh, dbt, or comparable frameworks.
• Background building and supporting ETL or data ingestion pipelines across multiple source systems.
• Familiarity with enterprise data integration, data harmonization, and crosswalk design across complex applications.
• Proficiency with Git-based source control workflows and effective collaboration in team development environments.