Data Engineer for Generative AI & Lakehouse (Hybrid)

Korn Ferry Group; West Midlands; Hybrid; GBP 85,000 - 111,000; Full time

Job summary

Korn Ferry is seeking an experienced Data Engineer for building and maintaining high-throughput data pipelines using Apache Spark and cloud-native tools in a hybrid model in the United Kingdom. You will develop ETL/ELT workflows in AWS, enable AI/ML data ecosystems, and ensure governance and security across data integrations.

Responsibilities include optimizing SQL and Java/Scala code, contributing to lakehouse architectures, and collaborating with cross-functional teams to improve observability

Qualifications

• 5+ years of data engineering experience.
• Experience building high-throughput data pipelines.
• Strong SQL development skills and data modeling knowledge.
• Hands-on Spark development using Java or Scala.

Responsibilities

• Design, implement, and maintain high-throughput data pipelines using Spark and cloud-native tools.
• Develop ETL/ELT workflows for structured and unstructured data in AWS.
• Collaborate with AI/ML teams to enable data platforms for Generative AI and advanced analytics.
• Ensure data quality, governance, and monitoring across integrations.
• Enable data lakehouse architectures that support scalability and analytics.
• Write optimized SQL queries and scalable code in Java or Scala.
• Apply security principles and operational guidelines for secure, highly available solutions.
• Partner with cross-functional teams to improve automation and observability in data platforms.
• Troubleshoot complex data processing issues in distributed systems.

Job description

Korn Ferry is seeking an experienced Data Engineer for building and maintaining high-throughput data pipelines using Apache Spark and cloud-native tools in a hybrid model in the United Kingdom. You will develop ETL/ELT workflows in AWS, enable AI/ML data ecosystems, and ensure governance and security across data integrations.

Responsibilities include optimizing SQL and Java/Scala code, contributing to lakehouse architectures, and collaborating with cross-functional teams to improve observability