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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
Apply on the employer's site

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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 Zeta

Likely 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?