Responsibilities :

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.

Additional Responsibilities:

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.

Technical and Professional Requirements:

Iceberg, Doris, Trino