Job Opportunities API

Senior Data Engineer Who we are Jellyvision is redefining how organizations experience benefits by bringing everything together in one modern, intelligent home. With ALEX Home, we combine our award-winning ALEX® decision support with a flexible benefits administration platform, giving employers and employees a simpler, smarter way to manage benefits year-round. Our mission is to help organizations reduce complexity, lighten administrative burden, and drive real employee understanding and utilization without forcing rip-and-replace decisions. We meet teams where they are today and give them a clear path to what’s next. The people behind Jellyvision are creative problem solvers who care deeply about getting it right. We debate ideas, give real feedback, and sweat the details because those details are what turn complicated problems into great experiences for real humans. We’re a human-first company that trusts smart people to do great work. We value curiosity, kindness, and willingness to try new things, learn fast, and try again. You won’t just show up to do a job, you’ll help build what’s next, solve real problems, and have some fun doing it.  What’s the role? 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. What you’ll do to be successful Build and operate data pipelines 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 Assess and improve existing data systems 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. Build and maintain data infrastructure 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. Contribute across the data platform 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. Experience & skills you’ll need Required: 6–8+ years of data engineering experience with hands-on ownership of produc