Staff Data Engineer

People In AI is a rapidly scaling AI-driven software company seeking a Staff Data Engineer to enhance their core infrastructure for a large-scale data and automation platform. This role involves designing and optimizing distributed data platforms, improving performance and reliability, and collaborating with various teams to transform complex data systems into scalable capabilities.

Responsibilities:

• Design, build, and optimize large-scale distributed data platforms powering real-time and batch processing systems
• Improve performance, scalability, reliability, and cost efficiency across Spark workloads, streaming infrastructure, and cloud-native compute systems
• Build and maintain backend services, APIs, orchestration systems, and platform tooling supporting customer-facing applications and internal operations
• Develop and improve ingestion and normalization pipelines processing large volumes of evolving third-party data
• Partner closely with product, operations, and engineering teams to transform complex data systems into scalable platform capabilities
• Improve observability, monitoring, alerting, and operational reliability across the broader engineering ecosystem
• Contribute to AI-powered operational tooling, automation systems, and intelligent workflows supporting internal and customer-facing products
• Help shape platform architecture, engineering standards, and long-term technical direction while remaining deeply hands-on in implementation

Requirements:

• Strong experience building and operating distributed systems and large-scale data infrastructure
• Deep hands-on experience with technologies such as Spark, Kafka, Flink, Airflow, Kubernetes, and cloud-native data platforms
• Strong backend engineering experience building APIs, microservices, and platform services using Python, Java, or Scala
• Experience operating real-time streaming systems and high-throughput data pipelines in production environments
• Comfortable working with evolving schemas, messy third-party data, and operationally complex systems
• Strong systems design and architectural thinking combined with hands-on implementation ability
• Strong ownership mentality with the ability to operate autonomously in small, highly collaborative teams
• Comfortable balancing speed, scalability, reliability, and pragmatic engineering tradeoffs
• Startup or high-growth engineering environment experience preferred
• Interest in AI-native workflows, operational automation, and modern platform engineering practices