Analytics Engineer

This role is directly client-facing. It is not a behind-the-scenes technical function that hands findings to someone else to deliver. As the product suite and client engagement work grow, this role owns making sure a metric means the same thing everywhere it appears, one central data dictionary, one set of canonical definitions, applied consistently across products and engagements, and owns standing in front of the client to make that real: educating their team on what it means, setting up and training them on the dashboards built from it, defending the methodology when someone on their side pushes back, and being accountable for whether they're actually getting the business outcome they need, not just whether the underlying query is correct.

Like everyone here, you will write code, including building the data pipelines this role needs, with AI coding tools doing a lot of the heavy lifting. That code goes through the company's established review process: every submission is reviewed by an experienced engineer before it ships, with structured feedback and real mentorship built in, not a rubber stamp and not sink-or-swim.

There's real training and support behind that process specifically so people can grow into it with confidence, whatever level they're starting from. You will also submit feature requests and input to the products this dictionary feeds, the same way everyone in the company does.

What makes this role distinct isn't that pipeline work or product input are off limits, it's that your primary focus is the dictionary and the validation discipline behind it, delivered directly to clients, not just internal teams. Validation means more than confirming a number is technically correct. You're expected to understand why a given dashboard or metric exists, what story it's telling the client, how they actually use it to make a decision, and what else should be looked at alongside it to give a complete picture, and then sit across the table from that client and walk them through it.

A dashboard that's technically accurate but tells the wrong story, or a client team that was never properly trained on what they're looking at, is still a problem this role owns. If you'd rather confirm a number ties out and call it done than have that conversation directly with the client, or if the idea of defending a methodology decision to a skeptical CFO makes you want to hand it off to someone else, this role is not a fit.

This role is directly client-facing. It is not a behind-the-scenes technical function that hands findings to someone else to deliver. As the product suite and client engagement work grow, this role owns making sure a metric means the same thing everywhere it appears, one central data dictionary, one set of canonical definitions, applied consistently across products and engagements, and owns standing in front of the client to make that real: educating their team on what it means, setting up and training them on the dashboards built from it, defending the methodology when someone on their side pushes back, and being accountable for whether they're actually getting the business outcome they need, not just whether the underlying query is correct.

Like everyone here, you will write code, including building the data pipelines this role needs, with AI coding tools doing a lot of the heavy lifting. That code goes through the company's established review process: every submission is reviewed by an experienced engineer before it ships, with structured feedback and real mentorship built in, not a rubber stamp and not sink-or-swim.

There's real training and support behind that process specifically so people can grow into it with confidence, whatever level they're starting from. You will also submit feature requests and input to the products this dictionary feeds, the same way everyone in the company does.

What makes this role distinct isn't that pipeline work or product input are off limits, it's that your primary focus is the dictionary and the validation discipline behind it, delivered directly to clients, not just internal teams. Validation means more than confirming a number is technically correct. You're expected to understand why a given dashboard or metric exists, what story it's telling the client, how they actually use it to make a decision, and what else should be looked at alongside it to give a complete picture, and then sit across the table from that client and walk them through it.

A dashboard that's technically accurate but tells the wrong story, or a client team that was never properly trained on what they're looking at, is still a problem this role owns. If you'd rather confirm a number ties out and call it done than have that conversation directly with the client, or if the idea of defending a methodology decision to a skeptical CFO makes you want to hand it off to someone else, this role is not a fit.

This person takes healthcare claims and finance data, from clearinghouse…