Our client is seeking a Data Engineer to manage the data modelling and reporting layer of a complex, multi-tenant analytics platform.
The role will focus heavily on Python, BigQuery, Dataform, and dimensional data modeling, including the use of Kimball methodology to transform raw data into reliable, scalable, and analytics-ready datasets for business intelligence and reporting.
What You'll Be Doing
- Design and develop dimensional data models that convert raw and intermediate data into analytics-ready datasets.
- Build, maintain, and improve SQL transformation workflows using Dataform and BigQuery.
- Apply Kimball data warehousing concepts when defining facts, dimensions, relationships, and reporting structures.
- Develop reusable and maintainable SQL logic for complex transformations, calculations, joins, and aggregations.
- Investigate unfamiliar datasets through data profiling and exploratory analysis, identifying anomalies, structural issues, relationships, and potential business use cases.
- Establish validation and testing processes to identify incomplete, inconsistent, or inaccurate data before it reaches reporting environments.
- Improve BigQuery workloads through appropriate query design, partitioning, clustering, and other performance and cost-management techniques.
- Create and maintain clear documentation around data models, transformation logic, dependencies, and data definitions.
- Prepare curated datasets and semantic structures that can be consumed effectively by BI and visualization platforms.
- Use Git-based development practices to manage changes to SQL and transformation code.
What We're Looking For
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Strong hands-on experience with dimensional data modeling and Kimball methodology, including fact and dimension design.
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Advanced proficiency in SQL, including complex joins, CTEs, window functions, aggregations, and data transformations.
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Deep practical experience with Google BigQuery, including query performance, partitioning, clustering, and cost optimization.
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Hands-on experience with Dataform for building, testing, and managing production data transformation workflows.
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Solid understanding of data warehouse architecture, including star schemas, snowflake schemas, and data marts.
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Experience performing exploratory data analysis (EDA) and data profiling to understand datasets and identify data quality issues.
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Strong knowledge of data quality, testing, and validation practices for analytical datasets.
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Experience preparing and structuring data for BI and visualization platforms such as Looker, Tableau, or Power BI.
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Proficiency with Git and version control for managing SQL and data transformation code.
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Exposure to BigQuery ML (BQML) or similar machine learning capabilities within a data warehouse environment is an advantage.
Desirable Experience
Experience with BigQuery ML (BQML), particularly developing or operationalizing machine-learning models directly within the BigQuery environment, would be an advantage.
Work Setup
- Full-time, remote
- Night Shift
- Permanent