Company Description:
- CREATIVITY IS OUR SUPERPOWER. It’s our heritage and it’s also our future. Because we don’t just make toys. We create creative products and experiences that inspire fans, entertain audiences and develop children through play. Mattel is at its best when every member of our team feels respected, included, and heard—when everyone can show up as themselves and do their best work every day. We value and share an infinite range of ideas and voices that evolve and broaden our perspectives with a reach that extends into all our brands, partners, and suppliers.

Mattel is seeking a GCP Data Engineer, based out of our Technology & Innovation Center in Hyderabad, India, reporting to the IT Director for Enterprise Data and Analytics.

This role will lead the design and development of scalable cloud-based data pipelines using tools like BigQuery, Python, SQL, DBT, and Airflow. You will drive detailed designs decisions, ensure data quality, and collaborate with cross-functional teams to deliver trusted, analytics-ready datasets.



This role also includes mentoring junior engineers and setting engineering best practices to support Mattel’s enterprise data strategy.

What Your Impact Will Be:
- 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.
- Automate and monitor complex data workflows using Airflow/Cloud Composer, ensuring dependable pipeline orchestratio

📌 Data Engineer (Hyderabad)
🏢 Mattel
📍 Hyderabad