Iceberg, Doris, Trino

Iceberg, Doris, Trino

Iceberg, Doris, Trino Preferred Qualifications: • Proven experience designing lakehouse architectures, including table layout strategies, compaction approaches, and multi-engine interoperability. • Advanced Trino optimization experience (query plans, statistics, resource groups, connector tuning) for high concurrency and large datasets. • Strong Doris operational expertise including ingestion optimization, indexing strategy, and workload management for BI/analytics use cases. • Experience strengthening platform reliability through observability, capacity planning, and performance benchmarking. • Ability to lead cross-team technical initiatives, influence architecture decisions, and communicate trade-offs clearly to technical and non-technical stakeholders Key Responsibilities: • Lead architecture and implementation of lakehouse solutions using Iceberg for table management, governance, and scalable storage patterns. • Design and optimize distributed query workloads using Trino, including catalog configuration, connector strategy, and query performance tuning. • Build and operate high-performance analytics serving layers using Doris, focusing on ingestion patterns, schema design, and workload isolation. • Drive end-to-end data pipeline execution leveraging Spark for batch processing, transformations, and data quality enforcement. • Establish standards for data modeling, partitioning, compaction, file sizing, and lifecycle management to improve cost and performance. • Own production readiness: monitoring, alerting, incident response, root-cause analysis, and continuous performance improvements across the stack. • Collaborate with stakeholders to translate analytical needs into scalable technical designs, delivery plans, and measurable outcomes. • Mentor engineers, conduct design/code reviews, and guide best practices for reliability, maintainability, and secure data access. Minimum Qualifications: • BTECH, MTECH, MCA, or MSC in Computer Science, Engineering, or a related field. • 8–12 years of experience in data engineering, data platform, or analytics infrastructure roles with leadership/ownership responsibilities. • Strong hands-on expertise with Iceberg, Doris, and Trino in production environments, including performance tuning and operational support. • Strong experience with Spark for scalable data processing and pipeline development. • Solid understanding of distributed systems, data storage formats, query optimization, and production troubleshooting practices.