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. What this role actually does 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 files, PMS/EHR systems, bank and GL data, and other source systems, and maps it into the company's central data dictionary, so that a term like denial rate, net revenue, or clean claim rate has exactly one definition that every product and engagement uses. This spans both the internal product suite (InsightFlow, NDS DataVault, ExoComp, ReconIQ, Payer-IQ, ClaimVision, Denial-IQ, Finance-IQ) and live client engagement work, so a metric means the same thing whether it shows up in a product dashboard or a client deliverable. This person is the direct point of contact for clients on the data dictionary and the dashboards built from it. That means personally leading setup and onboarding sessions with a client's team, training them on how to read and use their own reports, defending definitions and methodology when a client's finance or operations team pushes back, and validating with the client directly, not just internally, that a dashboard tells the right story and supports the decision it's meant to support. Client success on data and reporting is this role's direct responsibility, not a byproduct of technical correctness. Once a mapping exists, this person validates that dashboards and queries built on top of it actually display what the dictionary says they should, catching cases where a visualization has drifted from the canonical definition, pulls from the wrong source field, or silently breaks when an upstream system changes. This includes working directly with whoever built a given dashboard or report to get it corrected, and, where the client is the one relying on it, going back to the client to confirm the fix actually resolves what they were seeing. Just as important, this person owns whether a dashboard or metric actually does its job for the client relying on it: why it exists, what decision or conversation it supports, and what related numbers or context should sit alongside it so it isn't misread in isolation. That means working directly with client stakeholders, not only internal delivery leads, to understand how something is actually being used, and educating them until they can explain it back correctly themselves. This person also owns the data dictionary itself as a living document: adding new fields and definitions as the product suite and client engagements grow, retiring or correcting definitions that turn out to be wrong, and making sure the dictionary is the thing people actually check, not a document nobody opens. Where a mapping or validation need calls for a new pipeline or a fix to an existing one, this person builds it, using AI coding tools as part of that work and going through the standard code review process like everyone else. Where the dictionary or validation work surfaces something the product suite should handle natively, this person submits that as a feature request, the same as any other employee would. How we will know it is working Dictionary coverage. Every core metric and field used across the product suite and active client engagements has a documented, canonical definition in the data dictionary, tracked as a simple count of covered versus uncovered terms, reviewed quarterly. Mapping accuracy. Source-to-dictionary mappings are checked by audit sampling, pulling a sample of mapped fields each quarter and verifying them against source data directly, with a target of zero unexplained mismatches. Validation catch rate. Dashboards and queries are checked against the dictionary before they reach a client or executive, not after. Issues caught in validation, before external exposure, are tracked separately from issues caught after the fact, with the goal of catching the large majority before release. Client enablement. Every active client engagement has a documented setup and training session completed directly with the client's team, followed by a client-side comprehension check confirming their stakeholders can correctly explain what their core dashboards show and why. Tracked as a count of engagements with a confirmed, client-side check versus those without, not by whether a session was merely scheduled. Narrative and usage review. For each core client-facing dashboard, a documented, one-paragraph narrative exists (what it shows, why it matters, what decision it supports, and what related metrics should be checked alongside it), confirmed directly with the client stakeholder who relies on it, not written in isolation. Coverage is tracked the same way as dictionary coverage: a count of dashboards with a confirmed narrative versus without one. Turnaround. New mapping requests and validation checks are completed within an agreed timeline, with delays flagged early rather than discovered when a client asks why a dashboard still isn't live. How compensation is tied to that Base salary is $95,000 to $115,000, set based on experience, plus a bonus of up to 10% of base. The bonus is split across the measures above: 15% tied to dictionary coverage, 15% to mapping accuracy, 15% to validation catch rate, 20% to client enablement, 20% to narrative and usage review, and 15% to turnaround. Each piece pays out based on where the actual, recorded number lands relative to target, not an all-or-nothing cutoff, so partial performance still earns a partial payout. This role also comes with unlimited PTO, a genuine culture of learning, stretching, and growing rather than staying in a narrow lane, and a mission worth being part of: helping transform healthcare and ease the administrative burden on the people delivering it. Who this is This role fits someone with real hands-on experience in data mapping, data governance, or analytics engineering, ideally with exposure to healthcare claims data (835/837 clearinghouse files, PMS/EHR systems) and financial data (GL structure, chart of accounts). SQL fluency is necessary, and comfort validating a dashboard or report against source data, not just building one, matters as much as the technical skill list. This role also requires genuine comfort being the face of this work to clients: leading a training session, explaining a methodology decision to a skeptical CFO or VP of Revenue Cycle in person, and staying composed and credible when a client pushes back on a number rather than escalating it to someone else to handle. Prior experience in a client-facing technical, implementation, consulting, or customer success role is necessary, not just an internal-facing data background. This person needs to be comfortable being the one who tells a client a number is wrong, and needs to document decisions clearly enough that the dictionary is actually usable by people who weren't in the room when a definition was decided. Just as important, this person needs real curiosity about the business side of the data: what a dashboard is actually for, who relies on it, and what decision it's supposed to support, not just whether the query behind it is correct. Comfort operating under HIPAA, HITECH, and SOC 2 Type II requirements is necessary given the healthcare client base. Equal Employment Opportunity Statement NDS does not discriminate against otherwise qualified applicants for any of its open job opportunities, specifically in regard to age, race, sex, sexual identity, religious belief, national origin, disability, veteran status, or any other status protected by #J-18808-Ljbffr National Data Solutions