Mattel
From Mattel's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
This role will lead the design and development of scalable cloud-based data pipelines using tools like BigQuery, Python, SQL, DBT, and Airflow.
Mattel is a leading global toy and family entertainment company and owner of one of the most iconic brand portfolios in the world. We engage consumers and fans through our franchise brands, including Barbie, Hot Wheels, Fisher-Price, American Girl, Thomas & Friends, UNO, Masters of the Universe, Matchbox, Monster High, MEGA and Polly Pocket, as well as other popular properties that we own or license in partnership with global entertainment companies. Our offerings include toys, content, consumer products, digital and live experiences. Our products are sold in collaboration with the world’s leading retail and ecommerce companies. Since its founding in 1945, Mattel is proud to be a trusted partner in empowering generations to explore the wonder of childhood and reach their full potential.
Mattel’s award-winning workplace culture has been recognized by Forbes, Fast Company, Newsweek, Great Place to Work, TIME, and more.
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.
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+ years of hands-on experience in data engineering with strong expertise in data warehousing, pipeline development, and analytics on cloud platforms.
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.
Tools like Ascend.io, Databricks, Fivetran, or Dataflow.
Data cataloging/governance tools (e.g., Collibra).
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.
Ability to adapt quickly to new technologies and lead adoption across teams.
Excellent communication skills and ability to influence cross-functional teams.
Good experience on Agile Methodologies like Scrum, Kanban, and managing IT backlog.
Be a “go-to” expert for data technologies and solutions.
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