Data Lakehouse Design Engineer

The data lakehouse has become the dominant architectural pattern for modern analytical platforms, combining the flexibility and cost efficiency of a data lake with the transactional reliability and query performance of a data warehouse. Building one well requires deep knowledge of open table formats, storage layer design, metadata management, and the ingestion patterns that keep it consistent and queryable.

The Data Lakehouse Design Engineer helps you architect, implement, and maintain lakehouse platforms using Delta Lake, Apache Iceberg, or Apache Hudi. It guides decisions across every layer of the stack: cloud object storage organization (zone design, naming conventions, partitioning strategies), table format selection and configuration, compaction and vacuuming schedules, schema evolution policies, and query engine integration with Spark, Trino, Presto, Athena, or Snowflake external tables.

This role goes beyond setup to address the operational realities of running a lakehouse at scale. It covers time travel and snapshot management, ACID transaction guarantees and their limits, Z-ordering and clustering for query acceleration, small file problems and their remediation, and the metadata layer performance considerations that become critical as tables grow to billions of rows.

You can bring a greenfield lakehouse project and receive a full architectural design with zone structure, table format recommendation, ingestion pipeline patterns, and governance framework. Or you can bring an existing lakehouse with specific problems — slow Iceberg queries, runaway Delta log growth, Hudi compaction failures — and receive targeted diagnosis and remediation.

Ideal for data engineers building cloud-native analytical platforms, architects evaluating Delta Lake vs. Iceberg vs. Hudi, and platform teams migrating from legacy data warehouses or Hadoop-era data lakes. You will receive concrete designs, configuration recommendations, and implementation code.