About the Role
The role involves developing and maintaining robust data infrastructure, enabling efficient data processing and analytics across the organization using modern data stack tools and cloud environments.
Responsibilities
- Design and implement scalable data pipelines and architectures
- Optimize data workflows for performance and reliability
- Collaborate with data scientists and analysts to support analytics needs
- Maintain and enhance the organization's data platform
- Ensure data accuracy, consistency, and accessibility
- Integrate data from multiple sources into centralized systems
- Support data governance and compliance standards
- Troubleshoot and resolve data pipeline issues
- Develop automation for data operations
- Work with cloud-based data services and storage solutions
- Implement data quality monitoring and validation
- Contribute to documentation and knowledge sharing
- Participate in code reviews and system design discussions
- Stay current with advancements in data engineering technologies
- Support disaster recovery and data backup strategies
- Collaborate on security and access controls for data systems
- Assist in performance tuning of large-scale datasets
- Work with streaming and batch data processing frameworks
- Integrate machine learning pipelines with data infrastructure
- Support platform observability and monitoring tools
Nice to Have
- Master’s degree in a technical field
- Experience with real-time data streaming technologies
- Advanced knowledge of Databricks optimization techniques
- Experience with Delta Lake architecture
- Familiarity with data mesh concepts
- Exposure to machine learning operations (MLOps)
- Certifications in cloud or data engineering platforms
- Public contributions to data engineering communities
- Experience leading small technical teams
- Knowledge of infrastructure as code (IaC) tools
Compensation
Competitive salary and benefits package
Work Arrangement
Remote
Team
Collaborative data engineering team focused on scalable data platforms
Why Join Us
- Opportunity to work on large-scale data challenges in a flexible remote environment
- Exposure to cutting-edge data technologies and cloud-native architectures
- Support for professional growth and technical leadership development
Technology Stack
- Databricks, Apache Spark, SQL, Python
- AWS, Azure, or GCP cloud infrastructure
- Git, CI/CD, Airflow, Docker, Kubernetes
