Staff Data Engineer

Staff Data Engineer, Data Platform & AI

Compensation: $230,000–$260,000 base + equity

Location: Remote, United States

A high-growth AI platform company is hiring a Staff Data Engineer to help scale the infrastructure powering its next generation of AI-driven products.

This role sits at the intersection of distributed systems, backend engineering, data infrastructure, and AI workflows. You’ll work on large-scale pipelines ingesting and processing hundreds of external data feeds that support real-time products, automation systems, and customer-facing AI experiences.

What You’ll Do

• Scale and optimize distributed data pipelines and streaming systems
• Improve platform reliability, observability, and infrastructure performance
• Build backend services and APIs in Python and/or Java
• Work across Spark, Kafka, Kubernetes, Airflow, and AWS infrastructure
• Drive technical direction while remaining hands-on in implementation
• Partner cross-functionally with product and engineering teams
• Contribute to AI-powered operational tooling and automation systems

What You’ll Bring

• Staff-level experience building large-scale data platforms or distributed systems
• Strong Spark/Flink and Kafka expertise
• Experience with Python or Java microservices
• Cloud and Kubernetes infrastructure knowledge
• Strong systems design and distributed systems fundamentals
• Experience working with complex, evolving third-party data
• Interest in AI systems, automation, and intelligent workflows

Tech Stack

Python • Spark • Kafka • Airflow • AWS • Kubernetes • EMR/EKS • Distributed Systems • AI Infrastructure

Why Join?

• High-impact staff-level ownership in a lean engineering team
• Real-world scale and complex infrastructure challenges
• Strong AI investment and product momentum
• Fully remote within the US
• Competitive compensation + equity

About People In AI

People In AI partners with leading AI and infrastructure companies to connect exceptional engineers with high-impact opportunities across machine learning, data platforms, and next-generation software systems.