Member of Technical Staff - Principal Data Infrastructure Engineer

• Architect and maintain scalable, reliable, and observable Big Data Infrastructure for mission-critical AI applications.
• Champion DevOps and SRE best practices-automated deployments, service monitoring, and incident response.
• Build a self-service big data platform that empowers data and platform engineers and researchers.
• Develop robust CI/CD pipelines and automate infrastructure provisioning using Infrastructure as Code tools (Bicep, Terraform, ARM).
• Collaborate with Data Engineers, Data Scientists, AI Researchers, and Developers to deliver secure, seamless big data workflows.
• Lead technical design reviews and uphold a clean, secure, and well-documented codebase.
• Proactively identify and resolve bottlenecks in data pipelines and infrastructure.
• Optimize system performance across storage, compute, and analytics layers.
• Partner with Security teams to enhance system security (IAM, OAuth, Kerberos).
• Embody and promote Microsoft's values: Respect, Integrity, Accountability, and Inclusion.

• Deep technical expertise
• A passion for automation and observability
• Fluency in distributed systems
• Creativity to design scalable solutions
• And just as importantly: empathy, collaboration, and a growth mindset, * Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, data modeling, or data engineering
• OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling, or data engineering
• OR equivalent experience., * 4+ years in Big Data Infrastructure, DevOps, SRE, or Platform Engineering.
• 3+ years of hands-on experience managing and scaling distributed systems-from bare-metal to cloud-native environments.
• 2+ years deploying containerized applications using Kubernetes and Helm/Kustomize.
• Solid scripting and automation skills using Python, Bash, or PowerShell.
• Proven success in CI/CD pipeline management, release automation, and production troubleshooting.
• Experience working with Databricks for scalable data processing and analytics.
• Familiarity with security practices in infrastructure environments, including IAM, OAuth, and Kerberos administration.
• Proven experience with cloud-native infrastructure across Azure, AWS, or GCP.
• Hands-on expertise with modern data platforms like Databricks, including:
• Deep understanding of data storage and processing technologies:
• Relational & NoSQL databases
• Key-value stores.
• Spark compute engines.
• Distributed file systems (e.g., HDFS, ADLS Gen2).
• Messaging systems (e.g., Event Hub, Kafka, RabbitMQ).
• Capacity planning and incident management for large-scale big data systems.
• Solid collaboration history with Data Engineers, Data Scientists, ML Engineers, Networking, and Security teams.
• Familiarity with modern web stacks: TypeScript, Node.js, React, and optionally PHP.
• Exposure to agentic workflows, deep learning, or AI frameworks.
• Practical experience integrating LLMs (e.g., GPT-based models) into daily workflows-automating documentation, code generation, reviews, and operational intelligence.
• Solid grasp of prompt engineering techniques to design, optimize, and evaluate interactions with LLMs.
• Demonstrated ability to troubleshoot and resolve complex performance and scalability issues across infrastructure layers.
• Excellent interpersonal and communication skills, with a solid passion for mentorship and continuous learning.
• Experience applying LLMs to DevOps workflows, enhancing incident response, and streamlining cross-functional collaboration is a solid advantage.

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