Data Engineer

Description

We are looking for a Data Engineer to join a team in Santa Monica, California on a Long-term Contract assignment. This position focuses on building dependable data solutions that connect multiple sources, support reporting needs, and improve access to trusted business information. The role offers a blend of pipeline development, dashboard ownership, data quality work, and cross-functional collaboration with partner teams.

Responsibilities:
Design, validate, and release data pipelines that bring together information from varied source systems within an existing ingestion environment.
Create and support connections to external platforms, APIs, and vendor tools so data remains current, consistent, and available for analysis.
Oversee daily pipeline health, investigate processing issues, and partner with senior engineers to address failures and improve reliability.
Develop and maintain roughly 10 business intelligence dashboards used by 2 to 3 stakeholder groups, keeping reporting useful and aligned with team needs.
Improve dashboard quality by reviewing accuracy, optimizing performance, enhancing usability, and retiring reports that no longer provide value.
Write and maintain data validation checks, including tests at the source level to strengthen data quality and reduce downstream issues.
Support data modeling and transformation efforts using dbt to prepare clean, well-structured datasets for analytics and reporting.
Handle ad hoc data requests submitted through an intake process, providing timely answers to business questions with clear supporting analysis.
Produce and update documentation for pipelines, reporting assets, and operational workflows to promote maintainability and knowledge sharing.
Take part in peer code review by submitting thoughtful feedback and incorporating suggestions to maintain strong engineering standards.

Requirements

1 to 3 years of experience in data engineering, analytics engineering, or another data-centric technical role.
Strong SQL skills with the ability to query, transform, reconcile, and validate complex datasets.
Working knowledge of Python for data processing, automation, or engineering tasks.
Experience with BI and reporting platforms such as Power BI, Tableau, Looker, Sigma, or comparable tools.
Familiarity with cloud data warehouse technologies, including Snowflake, Redshift, or similar environments.
Exposure to dbt and modern transformation practices, or a demonstrated eagerness to learn quickly.
Understanding of version control workflows using Git.
Experience with technologies such as Apache Spark, Hadoop, Kafka, and ETL processes is beneficial.

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