Staff Data Engineer

Staff Data Engineer (AI-driven data infrastructure)

Role overview

As a Staff Data Engineer at Jellyfish, you will design and scale robust data infrastructure, define integration patterns, and drive the technical strategy for large-scale systems. You will shape both what gets built and how it is built, raising engineering standards across the team.

Responsibilities

• Define data architecture and long-term technical direction.
• Design and build data pipelines and an integrations platform.
• Drive third-party integrations and establish standards for faster, more reliable future integrations.
• Lead data export pipeline design/implementation to support diverse customer needs.
• Develop internal tooling to support and scale data operations.
• Make production-grade architectural decisions across end-to-end data infrastructure.

Requirements

• Expert Python engineer focused on reliable, maintainable systems at scale.
• Strong technical leadership: build consensus, unblock others, and improve engineers around you.
• Production experience with workflow orchestration tools such as Prefect, Airflow, or Dagster.
• Deep expertise building and scaling data pipelines and owning complex data infrastructure end-to-end.
• Experience with large-scale third-party API integrations (maintainable vs brittle).
• Exceptional communicator: write design docs, lead reviews, and explain tradeoffs.
• Infrastructure as Code experience (e.g., Terraform or CloudFormation) and ability to treat infrastructure as a product.
• Experience integrating AI/LLM tools (e.g., Claude, Copilot) and understanding their infrastructure demands.
• Systems thinking: consider user needs, business impact, and long-term maintainability.
• Experience working in a rapidly scaling startup with a balance of speed and technical rigor.
• Hands-on experience with developer tools integrations (e.g., Jira, GitHub, GitLab, CI/CD).
• Hands-on experience with Delta Lake and lakehouse architectures.

Location / time zone

• Must be located in EST or CST time zone.

Nice-to-haves

• Deep familiarity with engineering team workflows and tooling that improve (or hinder) execution.