Iceberg DBA

Tietoevry; Irving (TX); On-site; USD 130,000 - 170,000; Full time

Job summary

Tietoevry in Irving, TX is seeking an experienced Iceberg DBA / Lakehouse Operations Engineer to own the reliability of enterprise Iceberg data platforms. You will support a multi-engine Lakehouse environment across Spark, Hive, Impala, and Trino, focusing on table optimization, metadata management, and production support.

The role involves data modeling and Lakehouse architecture support, with on-call responsibilities, RCA, and security governance through Ranger RBAC, ensuring consistent query

Qualifications

• Experience with Apache Iceberg and Hive-based data lakes.
• Expertise in Iceberg table management, optimization, metadata, and performance tuning.
• Experience with large-scale TB/PB datasets.
• Strong knowledge of Spark SQL, Hive, Impala, NiFi, and Trino.
• Scripting in Python or Shell for automation and tooling.

Responsibilities

• Own day-to-day operations and reliability of Apache Iceberg tables at TB/PB scale.
• Perform table maintenance including: compaction, small-file management, snapshot expiration, and metadata cleanup.
• Optimize table performance through partitioning, file sizing, clustering, ordering, and pruning.
• Manage Iceberg metadata, snapshots, retention, archival, and time-travel capabilities.
• Provide L2/L3 production support, incident RCA, and preventive actions.

Skills

Apache Iceberg

Iceberg management

Spark SQL

Hive/Impala/Trino

Production support

Python/Shell

Partitioning optimization

Metadata management

Tools

Spark

Hive

Impala

Trino

Parquet/ORC

NiFi

Ranger RBAC

Job description

Position: Iceberg DBA / Lakehouse Operations Engineer

Location : Irving, TX

Visa Preference : USC/ GC's

Job Summary

We are looking for an experienced Iceberg DBA / Lakehouse Operations Engineer to own the reliability, performance, and operational integrity of enterprise-scale Apache Iceberg data platforms.

The role supports a multi-engine Lakehouse environment across Spark, Hive, Impala, and Trino, with a strong focus on table optimization, metadata management, performance tuning, production support, and Hive/Teradata-to-Iceberg modernization.

Key Responsibilities

• Own day-to-day operations and reliability of Apache Iceberg tables at TB/PB scale.
• Perform table maintenance including:
• Compaction and small-file management
• Snapshot expiration and metadata cleanup
• Optimize table performance through partitioning, file sizing, clustering, ordering, and pruning.
• Manage Iceberg metadata, snapshots, retention, archival, and time-travel capabilities.
• Ensure consistent query performance across Spark/CDE and Hive/Impala/CDW.
• Troubleshoot query failures, inefficient execution plans, metadata issues, and performance bottlenecks.
• Support data modeling and Lakehouse architecture, including 3NF and Bronze/Silver/Gold (Medallion) patterns.
• Ensure data accuracy through validation, reconciliation, and source-to-Iceberg checks.
• Provide L2/L3 production support, including P1/P2 incident resolution, RCA, and preventive actions.
• Support security and governance through Ranger policies and RBAC.
• Collaborate with Data Engineering, Platform, and Application teams on Lakehouse modernization and operational improvements.

Required Skills

• Strong hands‑on experience with Apache Iceberg and/or Hive-based data lakes.
• Strong expertise in Iceberg table management, optimization, metadata, and performance tuning.
• Experience managing large‑scale TB/PB datasets.
• Strong knowledge of Spark SQL, Hive, Impala, NiFi, and Trino.
• Strong understanding of partitioning, Parquet/ORC, and distributed query processing.
• Knowledge of data modeling and Medallion Lakehouse architecture.
• Experience with production support, troubleshooting, RCA, and on‑call environments.
• Scripting/automation skills using Python and/or Shell.

Preferred Skills

• Experience with Hive‑to‑Iceberg or Teradata‑to‑Iceberg migrations.
• Experience with Cloudera CDP, CDE, and CDW.
• Cloud experience with AWS and/or Azure.
• Experience with enterprise data modernization initiatives.
• Spark / Hive / Impala / Trino
• Cloudera CDP (CDE/CDW)
• Mission‑critical analytics and reporting workloads