Staff Software Engineer - Airflow

Cloudera•Austin, TX

14h•Remote

About The Position

The Data Platform Pillar is the bedrock of Cloudera’s technology, where we design and build the core components that let our customers store, manage, and process data with unmatched scalability, security, and performance. As a Staff Software Engineer, Airflow , you will be a key technical leader, responsible for the architecture, technical vision, and strategic evolution of the Apache Airflow-based workflow orchestration platform within the company’s data ecosystem. You will be expected to solve the most complex, ambiguous, and high-impact technical problems that span multiple teams or organizational boundaries. As a Staff Software Engineer you will: Technical & Architectural Leadership: Drive the multi-quarter technical roadmap and architecture for the Airflow platform, ensuring it is secure, highly scalable, reliable, and cost-efficient for enterprise-grade workloads. Complex Problem Solving: Design and implement solutions for the most challenging technical issues, such as extreme scale, multi-tenancy isolation, complex scheduling, and hybrid/multi-cloud deployment models. Cross-Functional Impact: Collaborate closely with product management, principal engineers, and other platform teams (e.g., Spark, Kubernetes) to define and deliver core orchestration capabilities that influence the entire data platform. Engineering Excellence: Define and champion best practices, performance optimization, and quality standards (observability, testing, and fault tolerance) for the Airflow service and its integrations. Mentorship: Mentor senior and junior engineers on complex technical design, best practices, and execution, elevating the overall technical capacity of the team and organization. Open Source: Maintain significant contributions and influence within the Apache Airflow open-source community, aligning the project’s roadmap with product strategy. Why this role matters: You will tackle complex distributed systems challenges, crafting the foundational software for the control and data planes that powers CDP and keeps it running at massive scale. Working at the forefront of hybrid and multi-cloud technology, you will empower data scientists, engineers, and analysts with the tools and infrastructure they need for advanced analytics and modeling. Collaboration is key, you will work alongside brilliant minds across product, data science, and engineering to drive innovation, standardize best practices, and shape the future of enterprise AI and data platforms. This is your chance to build the future of data and see your work make a global impact. This role is not eligible for immigration sponsorship.

Requirements

Bachelor’s degree in Computer Science or equivalent, and 6+ years of experience
Deep, hands-on knowledge of Apache Airflow internals (scheduler, executor, serialization, REST APIs) and complex DAG authoring/optimization.
Mastery in Python, some Java experience and extensive experience with core data platform technologies and cloud-native deployments (e.g., Kubernetes, Cloud Composer, AWS/GCP/Azure).
Demonstrated ability to drive design and architectural decisions with a focus on non-functional requirements (security, performance, high availability, fault tolerance).
Proven ability to lead and drive technical projects across multiple teams without direct reporting authority.

Nice To Haves

Experience defining the architecture for multi-tenant, service-oriented data platforms.
Significant contributions to Apache Airflow or related open-source projects.
Background in performance tuning and profiling large-scale Python and distributed applications.
Familiarity with data governance and security frameworks (e.g., Ranger, Kerberos) and their integration with workflow orchestration.

Responsibilities

Drive the multi-quarter technical roadmap and architecture for the Airflow platform, ensuring it is secure, highly scalable, reliable, and cost-efficient for enterprise-grade workloads.
Design and implement solutions for the most challenging technical issues, such as extreme scale, multi-tenancy isolation, complex scheduling, and hybrid/multi-cloud deployment models.
Collaborate closely with product management, principal engineers, and other platform teams (e.g., Spark, Kubernetes) to define and deliver core orchestration capabilities that influence the entire data platform.
Define and champion best practices, performance optimization, and quality standards (observability, testing, and fault tolerance) for the Airflow service and its integrations.
Mentor senior and junior engineers on complex technical design, best practices, and execution, elevating the overall technical capacity of the team and organization.
Maintain significant contributions and influence within the Apache Airflow open-source community, aligning the project’s roadmap with product strategy.