Cloud Lakehouse Platform Engineer

TD SYNNEX; Clearwater, Fremont (FL, CA); On-site; CAD 98,000 - 147,000; Full time

Job summary

Hyve Solutions is seeking an experienced Data Warehouse SE to evolve our data platform from Hadoop to a cloud-native Lakehouse built on Apache Iceberg. You will focus on platform engineering, running large-scale data infrastructure, not just pipelines, with emphasis on Spark tuning, metadata governance, and multi-engine data access.

Join a team delivering PB-scale workloads across cloud-native storage and Kubernetes environments, with Azure, Databricks, and Snowflake integrations to enable

Qualifications

• Experience with Spark execution planning and memory management.
• Hands-on with Iceberg, Hudi, or Delta Lake metadata, snapshots, and schema evolution.
• Kubernetes deployment of Spark workloads and resource scheduling.
• Cloud object storage experience (Azure/S3/MinIO) and performance considerations.
• Experience with Azure Databricks or Snowflake as a data platform.
• Strong programming skills in Java, Scala, or Python.

Responsibilities

• Design and operate an enterprise-grade Lakehouse on Iceberg for batch and streaming workloads.
• Orchestrate migration from Hadoop/HDFS to cloud-native storage with compute-storage separation.
• Develop core platform services: metadata, catalog, governance, and multi-engine access.
• Optimize Spark pipelines for PB-scale data processing and cost efficiency.
• Build cloud-native data platforms on Kubernetes and automate operations.
• Integrate with Azure, Databricks, and Snowflake for seamless data flow.

Skills

Spark optimization

Iceberg/Delta/Hudi

Kubernetes workloads

Cloud storage experience

Java/Python/Scala

Education

BSc/MSc in CS

Tools

Kubernetes

Databricks

Snowflake

Azure Blob Storage

S3

Job description

Hyve Solutions is seeking an experienced Data Warehouse SE to evolve our data platform from Hadoop to a cloud-native Lakehouse built on Apache Iceberg. You will focus on platform engineering, running large-scale data infrastructure, not just pipelines, with emphasis on Spark tuning, metadata governance, and multi-engine data access.

Join a team delivering PB-scale workloads across cloud-native storage and Kubernetes environments, with Azure, Databricks, and Snowflake integrations to enable