Mattel
Technology
SeniorDataEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“Senior Data Engineer at Mattel. Skills: Data Engineering, Cloud Data Warehousing, ETL/ELT Pipelines, Data Transformation. Lead development of data integration pipelines. Design and develop data integration pipelines”
Industry & Context.
Strategic problem-solving
What They're Looking For.
Must Have
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, Expert-level experience in Google BigQuery, Expert-level experience in Python for data processing, Expert-level experience in SQL for data wrangling, Expert-level experience in DBT for data transformation, Expert-level experience in Airflow / Cloud Composer for workflow orchestration, Proven experience building enterprise-grade ETL/ELT pipelines, Proven experience building scalable data architectures, Understanding of data quality frameworks, Understanding of data validation techniques, Understanding of data governance processes, Proficiency in Agile methodologies (Scrum/Kanban)
Nice to Have
Experience with Ascend.io, Experience with Databricks, Experience with Fivetran, Experience with Dataflow, Experience with data cataloging/governance tools, Experience with CI/CD tools, Experience with Git workflows, Experience with infrastructure automation, Experience with real-time/event-driven data processing, Strategic problem-solving skills, Ability to architect innovative solutions, Ability to adapt quickly to new technologies, Ability to lead adoption across teams, Good experience on Agile Methodologies like Scrum, Kanban, Managing IT backlog
What You'll Do.
Lead development of data integration pipelines
Design and develop data integration pipelines
Ingest data from enterprise systems
Build analytics-ready pipelines
Transform raw data into curated datasets
Implement transformation logic using DBT
Apply BigQuery best practices
Automate and monitor data workflows
Ensure dependable pipeline orchestration
Develop reusable Python and SQL code
Establish robust data quality checks
Establish testing strategies
Partner with architects and Technical leads
Establish best practices
Establish scalable frameworks
Establish reference implementations
Collaborate with cross-functional teams
Understand integration needs
Deliver business-aligned data solutions
Leverage modern ETL platforms
Contribute to technical documentation
Contribute to CI/CD workflows
Contribute to monitoring processes
Mentor junior engineers
Conduct peer code reviews
Lead technical discussions
How You'll Work.
Team & Collaboration
Cross-functional teams; Architects and Technical leads; Data analysts; BI developers; Product owners
Process & Methodology
Agile methodologies, Scrum, Kanban, Managing IT backlogs
Full Job Description
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 re
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