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. The role involves designing and optimizing distributed data platforms and collaborating with various teams to improve data systems and operational reliability.

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