Senior Analytics Engineer

Senior Analytics 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 Analytics Engineer, you'll own the layer between our data platform and the business — the models, metrics, and dashboards that turn raw data into something people trust and act on. Working closely with our platform data engineers, who own ingestion, pipelines, and infrastructure, you'll pick up where they leave off: shaping warehouse data into well-modeled, well-tested datasets in dbt, and building the dashboards and reporting in our BI layer that leadership, product, and operations rely on every day. The modeling is the craft; the dashboards are the point — you'll be responsible for both, and for making sure the numbers people see are ones they can trust.

This is a role for someone who loves the analytics engineering discipline and brings real engineering rigor to it — clear project structure, sensible conventions, and a considered point of view on what belongs in dbt versus the BI layer. You move fast and iterate frequently, and you're just as comfortable shaping something new as bringing order to what already exists.

What you’ll do to be successful

1. Build durable, trusted data models

Design and build data models in dbt — staging, intermediate, and mart layers — with sound structure, incremental logic, tests, and documentation

Apply dimensional modeling and grain discipline (star schemas, slowly-changing dimensions) so models are correct, performant, and reusable

Define core business metrics once, correctly, and in a way the whole organization can rely on

Classify sensitive data, maintain lineage and documentation, and define data-quality expectations at the model layer, partnering with the platform team on enforcement

Success looks like: Models are trusted, well-tested, and reused rather than rebuilt. Metric definitions are consistent everywhere they appear.

2. Own the semantic layer and metric definitions

Build and maintain the semantic layer between marts and dashboards, so a given metric means the same thing across every report

Partner with the business to define metrics and reconcile competing definitions into a single source of truth

Keep modeling documentation clear enough that anyone can understand what a metric means and how it's derived

Success looks like: The business argues about decisions, not about which number is right .

3. Deliver BI the business trusts

Build dashboards and reporting in our BI stack (Omni), grounded in well-modeled data rather than one-off queries

Handle row-level security, performance, and access so dashboards are reliable and appropriate for a regulated, multi-tenant environment

Translate ambiguous business questions into modeled metrics and clear, usable dashboards, and communicate data caveats plainly

When a dashboard looks off or a number doesn't reconcile, you can trace the issue back through the BI layer, the models, and the data to find the root cause and fix it

Success looks like: People use the dashboards you build to make real decisions, and trust the numbers when they do.

4. Bring structure and raise the quality bar

Establish clear conventions for how analytics work is structured, tested, and deployed — a coherent project layout, sound CI/CD, and a clear rationale for what's modeled in dbt versus the BI layer

Review analytics work across the team to keep models durable, tested, and consistent with our standards

Set and evolve modeling and BI patterns that make everyone's work better and faster, and share knowledge through code review, pairing, and documentation

Success looks like: Analytics work is consistent and easy to follow, quality holds even as we move quickly, and the team's models and dashboards get better over time.

Experience & skills you’ll need

Required:

7–9+ years in analytics engineering, data engineering, or BI, with deep hands-on ownership of the modeling and reporting layer

Deep dbt expertise: project design (staging/intermediate/marts), incremental models, tests, macros, exposures, and documentation

Strong dimensional and semantic modeling: dimensional design, grain decisions, metric definition, and building a reusable single source of truth

Advanced SQL and fluency with a cloud warehouse (Snowflake or comparable): complex transformations and performance-aware modeling

Strong BI/dashboarding skills in a modern tool (Omni, Looker, Power BI, Tableau, or similar), grounded in data modeling rather than one-off charts, including row-level security and performance

A disciplined approach to analytics engineering: clear project structure and conventions, solid Git and CI/CD practices, and sound judgment about where logic belongs across the dbt and BI layers

Ability to translate ambiguous business questions into modeled metrics and clear dashboards, and to communicate clearly with non-technical stakeholders

Experience using AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) as a genuine part of how you work

A fast, iterative working style: you ship well-built work and refine it rather than perfecting in isolation, you're comfortable making progress amid ambiguity, and you're just as effective improving something you've inherited as building something new

Nice to have:

Experience in a regulated industry (healthcare, insurance, benefits, financial services) with familiarity handling sensitive or PHI data

Experience with a dedicated semantic layer or metrics layer (dbt Semantic Layer or comparable)

Comfort with decision-support or analytical workloads — model inputs and outputs, statistical concepts — in support of data-driven products

Experience as an early or first dedicated analytics engineer on a team, bringing structure and durable patterns to a growing data landscape

Experience mentoring or setting standards for other analytics engineers or analysts

The Details

Location : Remote

Salary Range: $155,000 - $175,000

The salary range listed for this role reflects what we expect to pay, but where someone lands within that range depends on a few things, like their skills, relevant experience, qualifications, and how the role fits within our overall compensation philosophy, including internal equity and the budget for the role.

Compensation is more than just base salary. We also offer a comprehensive benefits package designed to support you at work and outside of it. Check them out here !

Our Talent Acquisition team will walk you through the full package and answer any questions throughout the interview process, so you'll know exactly what to expect.

Our commitment

Jellyvision is committed to continuous evolution and to building a workplace where everyone feels welcome, valued, respected, and empowered to do their best work.

It doesn’t matter your race, ethnicity, religion, age, disabi