Description
Responsible for developing and operating 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 errors and issues to ensure the best data platform experience.
Responsibilities
Develop and operate large-scale big data platforms using open source and other solutions.
Support critical applications including analytics, reporting, and AI/ML apps.
Optimize platform performance and cost efficiency.
Automate operational tasks for big data systems.
Identify and resolve production errors and issues to ensure platform reliability and user experience
Minimum Qualifications
3+ years of professional software engineering experience with large-scale big data platforms, including strong programming skills in Java, Scala, Python, or Go.
Proven expertise in designing, building, and operating large-scale distributed data processing systems with a strong focus on Apache Spark.
Hands-on experience with table formats and data lake technologies such as Apache Iceberg, ensuring scalability, reliability, and optimized query performance.
Skilled at coding for distributed systems and developing resilient data pipelines.
Strong background in incident management, including troubleshooting, root cause analysis, and performance optimization in complex production environments.
Proficient with Unix/Linux systems and command-line tools for debugging and operational support.
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).
A learning attitude to continuously improve the self, team, and the organization.
Solid understanding of software engineering best practices, including the full development lifecycle, secure coding, and experience building reusable frameworks or libraries.
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