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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Iceberg DBA / Lakehouse Operations Engineer - **Company:** PROPERTY CONSULTANT FIN SVC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Query Performance, Third Normal Form, Amazon Web Services, Apache HTTP Server, Microsoft Azure, Big Data, Cloud Computing, Cloudera Impala, Cluster Analysis, Data Architecture, Data Validation, Information Engineering, Data Governance, Data Integrity, Data Retention, Data Security, Data Structures, Apache Hive, Information Lifecycle Management, Python (Programming Language), Metadata, Meta-Data Management, Performance Tuning, Role-Based Access Control, DataOps, Cloudera, Simple Data Format, Data Streaming, Teradata SQL, Parquet, Scripting, Enterprise Software Applications, Data Ingestion, Apache Spark, Data Layers, Data Lakes, Apache Nifi, Data Management, Data Inconsistencies - **Published:** June 4, 2026 - **Apply:** https://www.dice.com/job-detail/35f920be-7db6-4da0-9b76-4a5e6f9a5358 ## About the Role · 4-6 years of experience in Big Data / Data Operations / DBA roles · Minimum 1+ year of experience with Apache Iceberg or similar table formats (Hive/Delta/Hudi) · 4+ years of experience with Cloudera ecosystem (CDP) · Hands-on experience with: o Iceberg table operations and maintenance o Spark SQL, Hive, or Impala · Experience in: o Production support and incident handling o Monitoring, troubleshooting, and operational support · Apply established data modeling and Lakehouse standards in day-to-day operations · Support: o Table structuring o Partition alignment with ingestion patterns · Assist in maintaining consistency of datasets across Bronze/Silver/Gold layers Required Skills · Strong hands-on experience with Apache Iceberg and/or Hive-based data lakes · Understanding of data modeling concepts (normal forms) and modern Lakehouse patterns (Medallion architecture) · Expertise in: o Table-level optimization and performance tuning o Large-scale data management (TB/PB scale) · Experience with: o Spark SQL, Hive, Impala, NiFI, Trino · Strong understanding of: o Partitioning strategies o File formats (Parquet/ORC) o Distributed query processing Preferred Skills · Experience with: o Hive-to-Iceberg or Teradata-to-Iceberg migration o Cloudera CDP (CDE/CDW) · Familiarity with: o Cloud platforms (AWS, Azure) · Scripting/automation (Python, Shell) What You'll Work On · Enterprise-scale Iceberg Lakehouse platform supporting multiple applications · Large-scale data modernization initiatives · Performance optimization and stability of mission-critical analytical workloads ## Description We are seeking a highly skilled Iceberg DBA / Lakehouse Operations Engineer to own the reliability, performance, and operational integrity of the Iceberg data layer powering enterprise analytics and business-critical applications. This role operates in a large-scale, multi-engine Lakehouse environment, supporting workloads across Spark, Hive, and Impala, and plays a key role in enterprise data modernization initiatives (Hive and Teradata * Iceberg). The ideal candidate brings deep expertise in Iceberg table operations, metadata management, and query performance optimization, ensuring consistent, high-performance data access across platforms in a cloud-based environment. This role is critical to ensuring data accuracy and performance-any degradation directly impacts downstream reporting, analytics, and business-critical decision-making. Key Responsibilities: Iceberg Data Layer Ownership & Operations · Own day-to-day operations of Apache Iceberg tables supporting multiple enterprise applications · Ensure data reliability, consistency, and availability across all Lakehouse workloads · Maintain operational integrity for datasets at multi-terabyte to petabyte scale Advanced Table Management & Optimization · Execute advanced Iceberg table maintenance and optimization strategies: o Compaction (minor/major) and small file mitigation o Snapshot expiration and metadata compaction to control metadata growth o Orphan file cleanup (vacuum) to maintain storage efficiency · Optimize data layout and performance through: o File size tuning and distribution strategies o Partition evolution and pruning optimization o Clustering and ordering techniques (e.g., Z-ordering or similar patterns) Data Modeling Standards & Lakehouse Design Alignment · Support and enforce data modeling best practices aligned with: o Normalized data structures (3NF) for source-aligned datasets o Medallion architecture (Bronze / Silver / Gold layers) for curated data flows · Ensure Iceberg table design aligns with: o Data ingestion patterns (raw vs curated layers) o Downstream consumption and performance requirements · Assist in structuring datasets to balance: o Data integrity and normalization o Query performance and analytical efficiency · Work with data engineering teams to ensure consistent implementation of layered data architecture across multiple applications Multi-Engine Query Performance & Consistency · Ensure consistent and performant query behavior across: o Spark (CDE) o Hive / Impala (CDW) · Troubleshoot and resolve: o Query performance bottlenecks o Metadata inconsistencies across engines o Inefficient execution plans and scan patterns Hive & Teradata Modernization Support · Play a key role in enterprise data platform modernization (Hive and Teradata * Iceberg) · Support: o Schema alignment and data type mapping o Data validation and reconciliation · Troubleshoot migration-related issues and ensure post-migration stability and performance Metadata & Data Lifecycle Management · Manage Iceberg metadata to ensure: o Efficient scaling and performance o Consistent table state across engines · Execute lifecycle operations: o Data retention and archival policies o Snapshot lifecycle management and cleanup o Time-travel optimization and maintenance Production Support, Incident Resolution & On-Call · Provide L2/L3 support for data-related production issues across Iceberg-based Lakehouse workloads · Participate in on-call rotation to support critical data platforms and ensure timely response to incidents · Respond to and resolve P1/P2 production incidents within defined SLAs, minimizing impact to downstream applications and reporting · Troubleshoot: o Data inconsistencies and reporting discrepancies o Query failures and performance degradation · Perform root cause analysis (RCA) and implement preventive measures to avoid recurring issues · Collaborate with platform and application teams during incident triage and resolution Security & Data Governance Support · Support fine-grained access control using: o Ranger policies and RBAC · Own and ensure data validation, reconciliation, and accuracy between source and Iceberg datasets · Ensure secure and compliant access to data across applications ## Related Videos - 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