Senior Software Engineer in Test - Anywhere Cloud

Cloudera•San Jose, CA

7d•Hybrid

About The Position

Senior Software Engineer in Test - Anywhere Cloud Team Overview The Anywhere Cloud (AWC) team is building Cloudera’s next-generation unified control plane. We are moving beyond traditional UI-driven workflows to an "AI-First" architecture. AWC enables the deployment of Data Services (like Spark, Trino, and Cloudera AI) across hybrid and multi-cloud environments. Our platform orchestrates complex Kubernetes infrastructures, foundational services (Service Mesh, Auth, Logging), and data engines. The Role This is essentially a data engineer role for the designation of Senior Software Engineer in Test . You will not just write tests; you will write automation and tools to validate Cloudera certified data pipelines. You will own the test strategy for designing, building, and executing custom data pipelines also known as Blue Prints You will leverage your deep domain expertise in data ecosystem engines like Spark, Kafka, Apache Polaris, Trino, Airflow and Lakehouse architectures to validate end to end use cases via custom blueprint. Your work will directly guarantee the functioning of the data pipeline for relevant use cases.

Requirements

AI First Mindset : Ability to learn and develop AI enabled test automation frameworks.
Engine SME Expertise : Hands-on understanding of modern compute and streaming engine internals like Spark, Kafka, Trino, Airflow
Kubernetes Expertise: Understanding of Kubernetes internals (CRDs, Controllers, Operators, Namespaces). You must understand how to debug and test complex Helm chart deployments and dependencies.
Language Proficiency: Expert-level proficiency in Python/Shell for scripting and automation.
Education: Bachelor’s or Master’s degree in Computer Science or equivalent experience.
Experience: 8+ years of software engineering experience with a focus on test automation, infrastructure, or backend development

Responsibilities

End-to-End Data Pipeline Validation: Design and execute test plans validating the end-to-end cluster creation flow on a kubernetes platform.
Data Modeling & Proactive Data Quality : Managing complex data modeling and schema drift, as well as embedding automated data quality checks and statistical anomaly detection directly into pipelines to shift away from reactive, manual quality processes.
Unified Data Governance Integration: Working with governance layers to ensure policies like tag-driven Attribute-Based Access Control (ABAC), column-level masking, row-level filters, and zero-code lineage ingestion (e.g., Octopai) are accurately enforced at the data layer.