Data Engineer

Data Architecture & Pipeline Development: Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using GCP services like Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer.

Data Warehousing: Develop and manage our central data warehouse in Google BigQuery. Implement data models, schemas, and table structures optimized for performance and scalability.

Data Processing & Transformation: Write clean, efficient, and robust code (primarily in SQL and Python) to transform raw data into curated, analysis-ready datasets.

Infrastructure Optimization & Scalability: Monitor, troubleshoot, and optimize our data infrastructure for performance, reliability, and cost-effectiveness. Implement BigQuery best practices, including partitioning, clustering, and materialized views.

Enable Data Accessibility & BI: Build and maintain curated data models that serve as the "source of truth" for business intelligence and reporting, ensuring data is ready for consumption by BI tools like Looker.

Data Governance & Quality: Implement automated data quality checks, validation rules, and monitoring to ensure the accuracy and integrity of our data pipelines and warehouse.

Collaboration: Work closely with software engineers, data analysts, and data scientists to understand their data requirements and provide the necessary infrastructure and data products.