Zeta
Data Reliability Engineer II
Basking Ridge, New Jerseyfull-timemidAdded yesterday
About this role
Zeta seeks a Data Reliability Engineer II to design, optimize, and maintain large-scale data lakes and warehouses that integrate data from multiple sources, supporting their cloud-native banking platform serving 25+ million cards globally.
What you'll do
- Develop and optimize data lakes and data warehouses handling multi-source data integration
- Ensure reliability, performance, and scalability of large-scale data infrastructure
- Monitor data quality and implement governance standards across platforms
- Collaborate with cross-functional teams to support banking and payments analytics needs
- Troubleshoot and resolve data pipeline and infrastructure issues
- Implement best practices for data management and security in financial systems
What they're looking for
- Data warehouse design and optimization
- Data lake architecture and management
- ETL/ELT pipeline development
- Cloud platforms (AWS/GCP/Azure)
- SQL and data querying
- Data quality and governance frameworks
- Monitoring and observability tools
- Python or Java programming
Benefits
- Work on banking technology serving 25+ million cards across 7 countries
- Join an engineering-first culture focused on ownership and innovation
- Access to modern cloud-native technology stack
- Great Place to Work certified company
- Global team with presence in India, US, EMEA, and Asia
Opens the official application on the employer’s site. No login required.
Zeta
Zeta builds cloud-native banking platform infrastructure that processes data at scale for millions of cards globally. The company is hiring data engineers and reliability specialists to design and maintain large-scale data lakes and warehouses that integrate multiple data sources.
View all jobs at ZetaLikely interview questions
- Describe your experience designing and managing large-scale data lakes or data warehouses. What sources and volume did you handle?
- How do you approach ensuring data quality and reliability across multi-source data pipelines?