Lead Data Analyst

Gifthealth Inc•Columbus, OH

1h•$132,000 - $165,000•Remote

About The Position

At Gifthealth, we're revolutionizing the way people experience healthcare by simplifying the process of managing prescriptions and health services. Our mission is to provide a seamless, personalized, and efficient healthcare experience for all our customers. We're a dynamic, innovative, and customer-centric company dedicated to making a positive impact on people's lives. We are seeking a Lead Data Analyst to own the analytical direction for a domain, drive its metric definitions, and lead a small team of data analysts. This position plays a key role in supporting the Analytics & Reporting department and reports to the Manager, Analytics & Reporting (with a dotted line to the Director, Data & Analytics), ensuring alignment with organizational goals, operational excellence, and compliance standards.

Requirements

A degree is optional. We evaluate demonstrated capability, not credentials, so equivalent hands-on experience is fully acceptable.
Typically 7 to 10 years of hands-on data analysis experience, with a track record as the senior analyst others checked their work with.
Formal people-management experience is welcome but not required; we will also consider a strong Senior Data Analyst ready for the step up.
A track record as the senior analyst others checked their work with on ambiguous business questions that ended in a decision someone acted on.
Deep SQL: comfortable in a warehouse with a few hundred dbt models, able to read other people's SQL critically, and checks for fan-out before trusting a total.
Experience setting metric definitions that other people then had to live with, documented well enough that they held.
Experience managing or leading analysts, or informal experience doing so and wanting the accountability that comes with the title.
Judgment about your own results: a number that looks great or alarming gets validated against a second source before it is shared.
Able to explain a finding to an operations lead, an account manager, and the Director in the same week, and to tell an account team what the data does not support without hedging or overclaiming.
Comfort working in an unfinished stack, with two migrations in flight and the ground moving.
Treats PHI, masking, BAAs, and role-based access as part of the daily workflow by default, not a compliance review at the end.
Strong understanding of healthcare data, PHI, and HIPAA requirements.
Proven ability to lead a small team and drive performance across the domain.
Excellent leadership, communication, and data-driven decision-making skills.

Nice To Haves

Pharmacy, specialty pharmacy, healthcare, or payer data experience, including claims, adjudication, prior authorization, NDCs, or the prescription lifecycle.
Manufacturer patient-support or hub program experience, especially where the manufacturer receives the reporting directly.
dbt experience to the point of writing and testing models yourself and reviewing a teammate's.
Looker and LookML experience, or experience migrating reporting off a legacy BI tool without freezing the business during the migration.
Python for analysis that SQL is not well suited to.
Experience owning a metric layer or definitions catalog that multiple teams depended on.

Responsibilities

Set the analytical direction for a domain: decide the canonical metrics, which sources are authoritative for which question, what gets modeled in dbt versus answered ad hoc, and the validation required before a result goes to a client or an executive.
Drive metric definitions for the domain into the dbt layer, in partnership with data engineering and BI, so that definitions live in models rather than in a Looker filter, a spreadsheet, or an analyst's head, and so any headline number can be traced to where it is computed and what it excludes.
Manage two to three data analysts as direct reports: 1:1s, review of their SQL and conclusions, growth plans, and performance conversations.
Own the sequencing of analysis in the domain, including what the business needs next, what recurring reporting can be retired, and what to say no to, negotiating directly with operations leads, account teams, and the Director.
Take on the hard questions in the domain that require patient-level reconstruction, an identified mechanism in the data, and an owned action, such as an unexplained metric shift or a client asking why two internal numbers disagree.
Keep shared metrics shared across domains by working with other leads and BI, escalating rather than forking a definition when there is disagreement.
Know where a join quietly drops rows across the underlying systems (NDP, LDZ, IDP, BestRx, and the entity-resolution layer that resolves identity across roughly thirty source systems), and teach that to the analysts on the team.
Analysts spend time on the distribution floor and listening to patient calls to understand the operation the data describes.
Periodic on-site visits to the Columbus distribution center and call center are expected, primarily during onboarding, to understand the operation the data describes.