Lead Data Platform Engineer

Responsibilities

• Design, deploy, and operate data platform services on Kubernetes in Microsoft Azure.
• Build and manage lakehouse infrastructure on Apache Iceberg backed by ADLS, including catalog configuration, table maintenance, schema evolution, and partitioning strategy.
• Operate and tune Trino and Spark clusters for performance, reliability, and cost efficiency.
• Develop and maintain data transformation pipelines using DBT, with sound modeling, testing, and documentation practices.
• Define and enforce standards for data access, security, governance, and multi-tenant resource management across ADLS and query engines.
• Implement observability for the platform, including monitoring, alerting, capacity planning, and incident response.
• Partner with data engineers, analysts, and platform teams to onboard workloads and resolve performance bottlenecks.
• Automate infrastructure and deployments using infrastructure-as-code and CI/CD.

## Required Qualifications

• Strong hands-on experience with **Kubernetes**, including deploying and operating stateful and distributed workloads.
• Strong hands-on experience with **Microsoft Azure Cloud**, including **ADLS**
• Hands-on experience with **Apache Iceberg** and Iceberg catalogs REST
• Expertise in distributed query and compute technologies, specifically **Trino** and **Spark**.
• Hands-on experience with **DBT** for data transformation and analytics engineering.
• Solid understanding of data lake and lakehouse architectures, file formats (Parquet, ORC), and query optimizatio

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