Data Engineer (Python x BigQuery) - WFH/Remote

POSITION SUMMARY

We are seeking a highly skilled Data Engineer to own the transformation and modeling layers of a new data warehouse project. This role is centered around an expert-level mastery of Google BigQuery (including Dataform) and Python. You will be responsible for taking raw data and architecting clean, reliable, and performant data models by applying Kimball methodology principles to power business intelligence and analytics. Your expertise in building sophisticated, scalable transformation pipelines with Dataform will be vital in transforming complex datasets into actionable insights and ensuring the analytical integrity of our dimensional data warehouse.

KEY RESPONSIBILITIES

• Lead the development, maintenance, and optimization of the entire data transformation lifecycle using Dataform to produce high-quality, trusted datasets.

• Build and manage scalable, well-documented, and tested data transformation pipelines that are critical for business reporting.

• Conduct Exploratory Data Analysis (EDA) on new and existing datasets to identify quality issues, understand data structures, and unlock business value.

• Write highly performant BigQuery SQL for complex queries, aggregations, and data manipulation, with a focus on cost and speed optimization.

• Implement rigorous data quality and validation checks within the transformation process to ensure accuracy and trust in our reporting datasets.

CORE REQUIRED SKILLS

- Kimball Methodology: Demonstrable, in-depth experience designing and building dimensional data warehouses using Kimball's principles.

• Dataform Expertise: Mastery of Dataform for developing, testing, and deploying complex SQL-based data transformation workflows in a production environment.

- Expert-Level SQL & Python: Demonstrable mastery of advanced SQL, including window functions, CTEs, and complex joins, with proficiency in Python for data engineering and transformation tasks.

• Google BigQuery: Deep, hands-on experience with BigQuery, including performance tuning, cost optimization, and partitioning/clustering.

• Database Schema Concepts: Strong, practical knowledge of data warehousing schemas (Star Schema, Snowflake Schema, Data Marts).

• Exploratory Data Analysis (EDA): Ability to analyze and profile datasets to derive insights and assess quality.

• Business Intelligence (BI) Tool Integration: Experience preparing data for consumption by BI tools like Looker, Tableau, or Power BI.

- AI Expertise: Demonstrable hands-on experience applying AI technologies and tools in data engineering, analytics, or data transformation workflows.

• BigQuery ML (BQML): Experience with building and deploying machine learning models using BQML is a significant plus.

• Version Control (Git): Proficiency in using Git for managing SQL and Dataform code.