Description: As a Data Platform SRE, you will be responsible for developing and operating our big data platform using open source or other solutions to aid critical applications, such as analytics, reporting, and AI/ML apps. This includes working to optimize performance and cost, automate operations, and identifying and resolving production issues to ensure the best data platform experience Responsibilities Design, develop, and automate: Build tools, frameworks and solutions to improve reliability, scalability, and efficiency across large scale distributed data platform systems. Monitor and maintain: Implement advanced monitoring and alerting for on-prem , cloud and workloads. Troubleshoot and solve: Support critical applications including analytics, reporting, and AI/ML apps. Respond to and resolve complex production incidents, and perform root cause analysis. Collaborate: Work closely with development and operations teams to integrate reliability best practices throughout the software lifecycle. Optimize: Proactively recommend improvements in architecture, deployment, and operations for distributed systems Minimum Qualifications: Experience: 5+ years in software site reliability engineering or software development roles. Programming: Proficient in at least one of Python, Golang, or Java. Skilled at coding for distributed systems and developing resilient data pipelines. Cloud Platforms: Hands-on experience with at least one major cloud platform (AWS, Azure, or Google Cloud Platform). Preferred Qualifications: Expertise in designing, building, and operating critical, large-scale distributed systems with a focus on low latency, fault-tolerance, and high availability. Experience with contribution to Open Source projects is a plus. Experience with multiple public cloud infrastructure, managing multi-tenant Kubernetes clusters at scale and debugging Kubernetes/Spark issues. Experience with workflow and data pipeline orchestration tools (e.g., Airflow, DBT). Understanding of data modeling and data warehousing concepts. Familiarity with the AI/ML stack, including GPUs, MLFlow, or Large Language Models (LLMs). Data Structures & Algorithms: Strong foundation and application experience. Distributed Systems: Solid understanding and hands-on experience managing at least one distributed system (e.g. Kafka, Spark, Flink etc. ). Solid understanding of software engineering best practices, including the full development lifecycle, secure coding, and experience building reusable frameworks or libraries. Problem Solving: Demonstrated ability to independently troubleshoot and resolve complex technical issues. Creative Thinking: A track record of proposing and implementing innovative solutions to technical challenges.

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