Senior Data Engineer, Jellyvision

• 6–8+ years of data engineering experience with hands-on ownership of production systems
• Strong pipeline design and orchestration skills in a production environment
• Advanced SQL: complex transformations, performance tuning, and debugging against a cloud data lake or warehouse
• Strong Python: production-grade code, scripting, testing, and debugging
• Working knowledge of infrastructure-as-code for provisioning and managing cloud data resources
• Familiarity with AWS data infrastructure: S3, IAM, and relevant managed services
• Experience inheriting and improving systems you didn’t build - developing working knowledge, identifying risks, and making them better
• Clear written communication: you can document a system, a process, or a recommendation so others can act on it independently
• Comfortable working across team boundaries with engineering and product on data needs
• Experience using AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) to accelerate engineering workflows

Desirable

• As a Senior Data Engineer, you'll be a hands-on engineer on a high-ownership data team.
• You'll build and operate data pipelines across both legacy and new platform infrastructure, contribute to the data systems that support them, and help improve the operational health of the stack as the platform evolves
• This is a high impact role on a small team. We're looking for someone who builds well, operates with care, and takes ownership of what they ship
• Design and build pipelines that support data movement across systems - ingestion, transformation, compliance, and cross-domain data flows
• Own pipeline operations end to end: monitoring, incident resolution, and data quality across both new and inherited workloads
• Make sound pipeline design decisions independently while working within the architecture the team has established
• Success looks like: Pipelines you build are reliable, well-tested, and documented. Pipelines you inherit are in better shape than when you found them
• Develop working knowledge of how existing infrastructure fits together by mapping data flows, dependencies and performance characteristics
• Improve documentation, observability, data quality, and operational standards across the systems you work in
• Identify technical debt and reliability risks and bring recommendations grounded in meaningful impact
• Success looks like: Systems you’ve touched are better documented, more reliable, and easier to operate. You surface problems before they become incidents
• Provision and manage the infrastructure your data workloads require, using established IaC practices and team standards
• Contribute to shared infrastructure as the platform evolves, building on new foundations as they become available
• Maintain and improve the reliability, performance, and cost-efficiency of the infrastructure you own
• Success looks like: You’re self-sufficient with the infrastructure your work requires. When the platform evolves, you’re building on it early
• Build enough working knowledge of the team's full system footprint to step in when priorities shift or teammates are unavailable
• Contribute to platform work as needed — you won't own the new build, but you should be ready to support it when the team needs flexibility
• Success looks like: When something comes up outside your primary area, you can step in without a lengthy handoff. That's what small teams require