About the Role
The Data Engineer will build and manage data infrastructure, develop ETL processes, and support analytics initiatives by delivering high-quality, accessible data across platforms.
Responsibilities
- Design and implement scalable data pipelines for ingestion and transformation
- Develop and maintain ETL workflows using modern data tools
- Ensure data accuracy, consistency, and availability across systems
- Collaborate with analytics and engineering teams to understand data needs
- Optimize data storage and query performance
- Monitor data pipeline health and resolve issues promptly
- Support data governance and compliance standards
- Integrate data from multiple internal and external sources
- Document data models, schemas, and processing logic
- Troubleshoot and debug data-related problems
- Improve data reliability and accessibility
- Work with cloud-based data platforms and services
- Implement automated testing for data workflows
- Contribute to data architecture planning
- Support deployment of data solutions in production environments
- Participate in code reviews and technical design discussions
- Stay current with data engineering best practices and tools
- Assist in migrating legacy systems to modern platforms
- Ensure security and privacy in data handling processes
- Provide technical guidance on data modeling and integration
- Collaborate on data quality monitoring and reporting
- Support business intelligence and reporting needs
- Work with version control and CI/CD pipelines
- Contribute to incident response for data platform issues
- Engage in agile project planning and delivery
Compensation
Competitive salary based on experience and location
Work Arrangement
Remote
Team
Cross-functional team focused on data infrastructure and analytics
Why Join Us
- Opportunity to work on large-scale data systems impacting global operations
- Supportive environment that values innovation and continuous learning
- Flexible work model with full remote capabilities
- Collaborative culture emphasizing teamwork and transparency
- Access to professional development and technical training
Technology Stack
- AWS, Azure, or GCP for cloud infrastructure
- Apache Spark and Kafka for data processing
- Snowflake or similar cloud data warehouse
- Airflow or similar orchestration tools
- Python, SQL, and Scala for development
Not specified
