Iceberg DBA / Lakehouse Operations Engineer

Job Title: Iceberg DBA / Lakehouse Operations Engineer

Location: Remote
Duration: Long-Term
Openings: 5

• L3 Senior Iceberg DBA: 2 positions
• L2 Iceberg DBA: 3 positions

Job Summary:
Seeking Iceberg DBA / Lakehouse Operations Engineers to support an enterprise-scale Lakehouse platform and large-scale data modernization initiatives (Hive/Teradata → Apache Iceberg). The role focuses on Iceberg table operations, performance tuning, metadata management, production support, and multi-engine query optimization.

Required Skills – L3 Senior:

• 10+ years Big Data/Data Engineering/DBA/Data Operations
• 2+ years hands-on Apache Iceberg production experience
• 6+ years Cloudera/CDP experience
• Expert knowledge of Iceberg table optimization, compaction, metadata management, and partition evolution
• Strong Spark, Hive, Impala performance tuning
• Experience with TB/PB-scale data environments
• P1/P2 production incident support and RCA
• Experience with Hive/Teradata → Iceberg migrations
• Data modeling and Medallion Architecture experience

Required Skills – L2:

• 4–6 years Big Data/Data Operations/DBA experience
• 1+ year Apache Iceberg or similar technologies (Hive/Delta/Hudi)
• 4+ years Cloudera/CDP
• Hands-on Iceberg table operations and maintenance
• Experience with Spark SQL, Hive, or Impala
• Production support, monitoring, and troubleshooting
• Understanding of partitioning, Parquet/ORC, and distributed query processing
• Knowledge of Bronze/Silver/Gold Lakehouse architecture

Preferred Skills:

• Hive/Teradata to Iceberg migration
• Cloudera CDP – CDE/CDW
• AWS/Azure cloud experience
• Python/Shell scripting
• NiFi and/or Trino
• Data quality, governance, validation, and reconciliation
• Ranger/RBAC security

Key Responsibilities:

• Manage and optimize Iceberg tables at enterprise TB/PB scale.
• Perform compaction, snapshot expiration, metadata cleanup, and orphan file cleanup.
• Optimize partitioning, file sizes, clustering, and query performance.
• Support Spark, Hive, and Impala workloads.
• Troubleshoot production issues and participate in L2/L3 on-call support.
• Handle P1/P2 incidents, RCA, and preventive actions.
• Support enterprise data modernization and migration initiatives.
• Maintain data reliability, consistency, security, and governance.