Iceberg, Doris, Trino

Key Responsibilities:

• Lead the architecture and implementation of lakehouse and analytics solutions using Iceberg, Doris, and Trino for scalable querying and reporting.
• Design and maintain Iceberg table layouts, partitioning strategies, schema evolution patterns, and data lifecycle management (compaction, snapshots, retention).
• Build and optimize distributed query workflows in Trino, including connector configuration, query tuning, resource governance, and workload management.
• Develop and optimize analytical data models and ingestion patterns leveraging Doris for high-performance OLAP workloads.
• Implement robust batch/stream processing pipelines using Spark, ensuring correctness, scalability, and cost efficiency.
• Establish performance benchmarks, monitor SLAs, and troubleshoot production issues across compute, storage, and query layers.
• Drive best practices for data quality, reliability, and operational excellence through automation, documentation, and runbooks.
• Mentor engineers, conduct design reviews, and lead technical decision-making aligned with long-term platform goals. Minimum Qualifications:
• Bachelor’s or Master’s degree in BTECH, MTECH, MCA, MSC or a related field.
• 6–8 years of experience in data engineering, analytics engineering, or building distributed data platforms.
• Strong hands-on expertise with Iceberg, including table design, partitioning, schema evolution, and maintenance operations.
• Strong hands-on expertise with Trino for federated/distributed querying, performance tuning, and operational troubleshooting.
• Strong hands-on expertise with Doris for OLAP use cases, data modeling, and query performance optimization.
• Proven experience building data pipelines using Spark in production environments.
• Solid understanding of distributed systems, query execution concepts, and data storage formats for analytics workloads. Preferred Qualifications:
• Experience designing end-to-end lakehouse architectures integrating Iceberg with multiple compute engines and downstream consumers.
• Advanced expertise in query optimization techniques (statistics, partition pruning, file sizing, caching strategies) across Trino and OLAP systems.
• Experience with Spark optimization (shuffle tuning, join strategies, adaptive execution) and building reusable pipeline frameworks.
• Strong operational ownership: monitoring, alerting, incident management, and capacity planning for analytics platforms.
• Ability to lead cross-team technical initiatives, influence standards, and improve platform adoption through enablement and documentation.