Senior Data Engineer

Mattel is seeking a Senior Data Engineer or Senior ETL Developer, 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:

- 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.

- 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.

- Python for data processing, orchestration, and scripting. - SQL for data wrangling, transformation, and query optimization. - DBT for developing modular and maintainable data transformation layers.

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.

- 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.

- Minimum 3+ 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.

- 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.

- Proficiency in Agile methodologies (Scrum/Kanban) and managing IT backlogs in a collaborative, iterative environment. - Preferred experience with:- Tools like Ascend.io, Databricks, Fivetran, or Dataflow. - Data cataloging/governance tools (e.g., Collibra).

- 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.

- 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.

- CI/CD tools, Git workflows, and infrastructure automation. - Real-time/event-driven data processing using Pub/Sub, Kafka, or similar platforms. - Strategic problem-solving skills and ability to architect innovative solutions.

- 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.

- 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.