GCP Data Engineer
mattel
Job Description
- Lead the development of scalable, secure, and high-performing data integration pipelines for structured and semi-structured data using Google BigQuery.
- Design and develop scalable data integration pipelines to ingest structured and semi-structured data from enterprise systems (e.g., ERP, CRM, E-commerce, Order Management) into a centralized cloud data warehouse using Google BigQuery.
- Build analytics-ready pipelines that transform raw data into trusted, curated datasets for reporting, dashboards, and advanced analytics.
- Implement transformation logic using DBT to create modular, maintainable, and reusable data models that evolve with business needs.
- Apply BigQuery best practices—including partitioning, clustering, and query optimization—to ensure high performance and scalability.
- Automate and monitor complex data workflows using Airflow/Cloud Composer, ensuring dependable pipeline orchestration and job execution.
- Develop efficient, reusable Python and SQL code for data ingestion, transformation, validation, and performance tuning across the pipeline lifecycle.
- Establish robust data quality checks and testing strategies to validate both technical accuracy and alignment with business logic.
- Partner with architects and Technical leads to establish best practices, scalable frameworks, and reference implementations across projects.
- Collaborate with cross-functional teams—including data analysts, BI developers, and product owners—to understand integration needs and deliver impactful, business-aligned data solutions.
- Leverage modern ETL platforms such as Ascend.io, Databricks, Dataflow, or Fivetran to accelerate development and improve observability and orchestration.
- Contribute to technical documentation, CI/CD workflows, and monitoring processes to drive transparency, reliability, and continuous improvement across the data engineering ecosystem.
- Mentor junior engineers, conduct peer code reviews, and lead technical discussions
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related technical field.
- Minimum 3-5 years of hands-on experience in data engineering with strong expertise in data warehousing, pipeline development, and analytics on cloud platforms.
- Expert-level experience in:
- Google BigQuery for large-scale data warehousing and analytics.
- Python for data processing, orchestration, and scripting.
- SQL for data wrangling, transformation, and query optimization.
- DBT for developing modular and maintainable data transformation layers.
- Airflow / Cloud Composer for workflow orchestration and scheduling.
- Proven experience building enterprise-grade ETL/ELT pipelines and scalable data architectures.
- Strong understanding of data quality frameworks, validation techniques, and governance processes.
- Proficiency in Agile methodologies (Scrum/Kanban) and managing IT backlogs in a collaborative, iterative environment.