Senior Data Engineer

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.

• What We’re Looking For:

• 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.

• 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.

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.

• Preferred experience with:

  • 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.

• 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.
• 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
• 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.
• Preferred experience with: Tools like Ascend.io, Databricks, Fivetran, or Dataflow. Data cataloging/governance tools (e.g., Collibra). CI/CD tools, Git workflows, and infrastructure automation. Real-t
• 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.