Senior Data Engineer

The Team As part of the Global Technology Organization, the Enterprise Data and Analytics (ED&A) Delivery Team is focused on enabling Mattel to become a data-driven organization, supported by a shared services platform that serves all business units: • Integration data from diverse enterprise systems such as ERP, ECOM, Order Management systems, CRM and operational platform into a centralized cloud-based data warehouse. • Building robust ETL/ELT pipelines that automate data ingestion, transformation, and delivery using modern tools and frameworks. • Delivering curated, business-ready datasets that support self-service analytics and strategic decision-making. • Enforcing data quality, testing, and governance standards across the enterprise. • Leveraging orchestration tools such as Airflow/Cloud Composer to ensure reliable and efficient pipeline scheduling and execution. • Partnering with cross-functional teams including data analysts, product owners, and BI developers to align data solutions with business needs. The Opportunity 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: • 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.