About the Role
This role involves leading the development and maintenance of data systems that support analytics and product decisions. The engineer will work closely with multiple teams to ensure data is accurate, available, and actionable.
Responsibilities
- Lead the design and implementation of data infrastructure
- Build and maintain scalable data pipelines
- Ensure data accuracy, reliability, and accessibility
- Collaborate with analytics and engineering teams
- Optimize data workflows for performance and efficiency
- Implement data governance and security standards
- Monitor system performance and troubleshoot issues
- Guide best practices in data engineering
- Support data modeling and warehouse architecture
- Evaluate and integrate new data technologies
- Drive automation across data processes
- Maintain documentation for systems and pipelines
- Work with stakeholders to understand data needs
- Improve data quality and validation processes
- Scale infrastructure to meet growing demands
- Promote reusable data components and patterns
- Ensure compliance with data privacy policies
- Lead code reviews and technical design sessions
- Mentor junior engineers on the team
- Coordinate with product teams on data requirements
- Contribute to long-term data strategy
- Integrate data from multiple sources
- Support real-time and batch processing systems
- Use cloud platforms for data solutions
- Balance innovation with system stability
Nice to Have
- Master’s degree in a technical field
- Experience with real-time streaming platforms
- Knowledge of machine learning pipelines
- Familiarity with data visualization tools
- Experience in high-growth startups
- Contributions to open-source data projects
- Leadership in cross-functional initiatives
- Public speaking or conference presentations
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid
Team
Collaborative and agile environment with engineering and product teams
What We Value
- Ownership of technical projects
- Clear communication across teams
- Continuous learning and improvement
- Building inclusive and supportive teams
- Delivering measurable impact
Technology Stack
- AWS and GCP for cloud infrastructure
- Snowflake for data warehousing
- Airflow for workflow orchestration
- Python and SQL for data processing
- Kafka for event streaming
- Terraform for infrastructure as code
- Docker and Kubernetes for deployment
Available
