Data Engineer

Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such as GCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer.

Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing.

Develop and manage our central data warehouse in Google BigQuery.

Design and implement data models, schemas, and table structures optimized for performance, scalability, and long-term maintainability.

Write clean, efficient, and maintainable SQL and Python code to transform raw data into curated, analysis-ready datasets.

Build reliable transformation workflows that support analytics, reporting, and data science initiatives.

Monitor, troubleshoot, and optimize data infrastructure to ensure high performance, reliability, and cost efficiency.

Implement BigQuery best practices, including partitioning, clustering, query optimization, and materialized views.

Build and maintain curated data models that serve as the “source of truth” for business intelligence and reporting.

Ensure data is optimized and readily accessible for BI tools such as Looker and other analytics platforms.

Implement automated data quality checks, validation rules, and monitoring frameworks to ensure the integrity and reliability of data pipelines and warehouse systems.

Establish processes for data governance, observability, and lineage tracking.

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