Principal Software Engineer - Intelligent Data Platform

About Zscaler

Zscaler (NASDAQ: ZS) accelerates digital transformation so that customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange is the company’s cloud-native platform that protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. 

With more than 10 years of experience developing, operating, and scaling the cloud, Zscaler serves thousands of enterprise customers around the world, including 450 of the Forbes Global 2000 organizations. In addition to protecting customers from damaging threats, such as ransomware and data exfiltration, it helps them slash costs, reduce complexity, and improve the user experience by eliminating stacks of latency-creating gateway appliances. 

Zscaler was founded in 2007 with a mission to make the cloud a safe place to do business and a more enjoyable experience for enterprise users. Zscaler’s purpose-built security platform puts a company’s defenses and controls where the connections occur—the internet—so that every connection is fast and secure, no matter how or where users connect or where their applications and workloads reside.

Summary:

We're seeking an experienced Principal Engineer to join our Cloud Data Platform team as a founding member. Your deep understanding of cloud-based data platforms and proven track record in building and scaling high-performance systems will be pivotal. As a Principal Engineer, you'll lead the development of our core data platform, shaping its design, implementation, and evolution to meet our dynamic business needs.

Responsibilities:

• Shape the long-term vision for our engineering platform, focusing on data transport, analytics, and business intelligence.
• Lead the design and development of our multi-tenant cloud-based data platform, driving innovation and influencing framework decisions.
• Maintain engineering excellence, ensuring 4-9s availability 24/7 in a full DevOps model by investigating root causes and implementing robust solutions.
• Collaborate with cross-functional teams to define and implement a scalable, high-performance, and secure data architecture aligned with current and future business needs.
• Assist in exploring and selecting cloud technologies across providers to construct a versatile cloud data platform.
• Stay abreast of emerging technologies to keep our platform at the forefront of industry advancements.
• Drive initiatives to enhance system reliability, availability, and performance through best practices in fault tolerance, monitoring, and automation.
• Mentor junior team members, fostering a culture of technical excellence, collaboration, and continuous learning.
• Operate within an Agile development environment, facilitating efficient delivery and adaptability.
• Debug and diagnose customer issues, taking ownership of problem resolution.
• Collaborate with product management to align technical strategies with business goals and contribute to the product roadmap.
• Actively participate in architectural discussions, providing insights and recommendations while ensuring adherence to established design principles.
• Evaluate and implement engineering processes and methodologies to enhance development efficiency, code quality, and delivery speed.

Qualifications:

• 10+ years of software engineering experience, specializing in cloud-based data platforms.
• Proficiency in Rest API, RDBMS, Document Stores, Kubernetes, and Docker.
• Experience with cloud services (e.g., AWS, Azure, GCP) and Data warehousing solutions (e.g, BigQuery, Snowflake, Databricks) is advantageous.
• Proficiency in one or multiple programming languages such as Java, Go, Python, etc
• Experience with stream processing frameworks such as Apache Beam, Dataflow, Flink, etc is a plus.
• Strong skills in designing scalable, fault-tolerant, and secure solutions.
• Proven track record in designing and implementing cloud-based data platforms, with expertise in distributed systems and data architecture.
• Familiarity with Kubernetes and containerized workloads.
• Experience with monitoring, tracing, and observability tooling and frameworks (e.g., Grafana, Prometheus).
• Proficiency in test frameworks and writing test units.
• Experience with machine learning and large language models is a plus
• Problem-solving mindset with a focus on quality and execution.
• Solid background in software engineering and system design.
• Experience working with product management and cross-team stakeholders to translate requirements into execution plans.
• Demonstrated success in mentoring and developing engineering talent.
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