Job Opportunities API

Senior Data Platform Engineer II 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 Platform Engineer, you’ll be a hands-on engineer on a small, high-ownership data team. You’ll work across the full data platform - relational, warehouse, and lakehouse systems - building and operating the pipelines that power compliance, analytics, and reporting workloads. This is a multi-hat role. Some days you’re building pipelines, other days you’re deep in schema design, improving infrastructure, or jumping into a production issue. You’ll contribute to the platform’s service layer, help evaluate new tools and approaches, and mentor other engineers on the team. We’re looking for someone who takes ownership of what they build, communicates clearly about system state and risk, and can work independently through ambiguity.  What you’ll do to be successful 1. Build and operate data pipelines Design and build pipelines that move data across systems - supporting data lake ingestion, compliance workloads, and cross-domain data flows Own pipeline operations end to end: monitoring, incident resolution, data quality, and documentation that lets any team member respond independently Identify technical debt and reliability risks and raise them with clear context and proposed next steps Success looks like: Pipelines run reliably. Known failure modes get fixed rather than worked around. You flag problems early and follow through on fixes. 2. Build and shape the data platform Design and maintain schemas across relational, warehouse, and lakehouse layers, working with application engineers and product to get data models right Build out the platform’s service layer, infrastructure-as-code, and data quality frameworks - this role spans design and implementation Keep platform documentation at a level where any team member can understand what exists, how it works, and where the risks are Over time, contribute to the analytics engineering layer, including modeling practices and semantic layer development Success looks like: The parts of the platform you own are well-documented, reliable, and improving over time. Schema changes land cleanly. Infrastructure is managed as code. 3. Inform architectural decisions and tooling evaluations Contribute to evaluations of the current platform against emerging architectures and tooling, helping produce trade-off analyses and recommendations Bring what you see day to day in the systems you operate into the team’s improvement roadmap and technical direction Track and report on platform health metrics: pipeline uptime, failure rates, data freshness, and cost trends Success looks like: You bring informed perspectives to architectural discussions grounded in hands-on experience. Your research and prototy