About the Role
The role involves building and optimizing data pipelines, ensuring data reliability, and supporting analytics initiatives through scalable engineering solutions.
Responsibilities
- Design and implement data pipelines for large-scale data processing
- Develop and maintain ETL workflows with attention to performance and reliability
- Collaborate with analytics and product teams to define data requirements
- Ensure data quality and consistency across systems
- Optimize data storage and retrieval mechanisms
- Support data warehouse architecture and evolution
- Monitor pipeline health and troubleshoot issues
- Integrate data from diverse source systems
- Implement data validation and error handling procedures
- Document data models, pipelines, and system dependencies
- Contribute to data governance and metadata management
- Work with streaming and batch processing frameworks
- Improve data accessibility for reporting and machine learning use cases
- Participate in code reviews and system design discussions
- Maintain security and compliance standards for data handling
- Evaluate and adopt new data technologies as needed
- Support incident response related to data systems
- Assist in capacity planning for data infrastructure
- Ensure scalability and fault tolerance in data solutions
- Collaborate on data modeling for operational and analytical needs
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model with flexible remote options
Team
Collaborative engineering team focused on scalable data systems
Tech Stack
- Primary tools include Apache Airflow, Spark, and BigQuery
- Infrastructure managed via Terraform and Kubernetes
- Code hosted on GitHub with automated testing pipelines
Growth Opportunities
- Access to conferences and training programs
- Internal mobility across engineering domains
- Mentorship from senior technical staff
Available for qualified candidates
