Description

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 optimization

Key Skills

Education

Any Gradute