Data Engineers

We are looking for a skilled Data Engineer to design, optimize, and maintain the data architecture that powers our BI environment.

This role focuses on building scalable BigQuery datamarts, optimizing data models, and ensuring reliable data pipelines to support analytics and reporting.

The ideal candidate has strong experience in cloud data warehousing, data modeling, and building analytics-ready datasets that enable efficient BI consumption.

• 3+ years of experience in Data Engineering or Analytics Engineering.
• Strong SQL expertise for analytical workloads.
• Experience with cloud data warehouses such as: BigQuery
• Amazon Redshift
• Snowflake
• Similar platforms
• Proven experience designing star-schema or dimensional data models.
• Experience building analytics-ready datamarts.
• Experience with dbt or similar data-transformation frameworks.
• Familiarity with Git-based workflows and CI/CD for data pipelines.
• Experience with BI tools such as: Looker
• Tableau
• Power BI
• Background working with retail or multi-location data environments.
• SQL
• Prompt Engineering
• Data Engineering
• Analytics Engineering
• BigQuery
• Data Modeling
• Data Warehousing
• Data Pipelines
• BI & Analytics
• dbt
• Git / CI/CD
• Salary: Up to $17/hr
• Originally posted on Himalayas

• Design, build, and maintain BigQuery datamarts and analytical tables for BI reporting.
• Develop scalable data models, including star schema, snowflake, and denormalized models.
• Optimize query performance and storage efficiency within cloud data warehouses.
• Collaborate with BI teams to ensure datasets align with semantic models and dashboards.

• Build and maintain data pipelines for data ingestion, transformation, and loading into analytics environments.
• Implement transformation workflows using SQL or frameworks such as dbt.
• Ensure data quality, validation, and consistency across datasets.

• Maintain data standards, naming conventions, and proper documentation.
• Implement governance practices, including schema management and version control.
• Monitor and troubleshoot pipeline failures and data inconsistencies.

• Work closely with BI specialists to ensure datasets support semantic-layer requirements.
• Deliver optimized datasets that minimize the need for heavy transformations within BI tools.