Senior Data Platform Engineer II

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 prototyping help leadership make confident decisions.

4. Mentor and raise the technical bar

Mentor peers and junior engineers through code review, pairing, and technical guidance

Help uphold engineering standards and collaborate cross-functionally with application engineering, product, and analytics as a reliable technical partner

Share knowledge through documentation and technical discussions

Success looks like: Engineers you’ve reviewed and paired with produce better work over time. Cross-team partners trust you for thoughtful input and follow-through.

Experience skills you’ll need

Required:

7–9+ years of data engineering or data platform experience with hands-on ownership of production systems

Experience building and operating a data lakehouse, data lake, or modern warehouse architecture (Snowflake, Databricks, or comparable)

Deep fluency with Apache Airflow or comparable orchestration: DAG design, task dependencies, sensors, and production operations

Solid understanding of open table formats (Iceberg, Delta, Hudi) and columnar storage (Parquet, Avro, ORC), including how format …