Manager, Analytics Engineering

Manager, Analytics Engineering

PANTHERx is the nation's largest rare disease pharmacy, and we put the patient experience at the top of everything that we do. If you are looking for a career in the healthcare field that embraces authentic dedication to patient care, you don't need to look beyond PANTHERx. In every line of service, in every position and area of expertise, PANTHERx associates are driven to provide the highest quality outcomes for our patients.

We are seeking team members who:

• Are inspired and compassionate problem solvers;
• Produce high quality work;
• Thrive in the excitement of the ever-challenging environment of modern medicine; and
• Are committed to achieving superior health outcomes for people living with rare and devastating diseases.

Join the PANTHERx team, and define your own RxARE future in healthcare!

Location:

Pittsburgh, PA (Hybrid)

Classification:
Exempt

Status:
Full-Time

Reports to:

Senior Director, Data & Analytics

Purpose

The Manager, Analytics Engineering leads the Analytics Engineering track within the Data & Analytics organization, owning the semantic layer between raw pipeline output and BI consumption. This function owns Gold-layer table design, Unity Catalog metric view definitions, Silver-to-Gold promotion logic, and data product development in a Databricks-native environment. By establishing governed, reusable metric views as the canonical source of truth for business metrics, the Analytics Engineering track enables consistent, trustworthy analytics delivery across BI, external partner feeds, and AI model inputs.

Responsibilities

Semantic Layer Ownership

• Owns the Unity Catalog semantic layer as the canonical, governed source of truth for all business metrics: metric view definitions, Silver-to-Gold promotion logic, data mart architecture, and data product development.
• Establishes the principle that every business metric is defined once as a metric view and made available, consistently and correctly, to every downstream consumer:
  Power BI dashboards via Direct Lake, external partner feeds, internal analytics, and AI model inputs.
• Sets and enforces standards for metric view design, naming conventions, versioning, and documentation, in alignment with Data Governance metadata standards and Unity Catalog access controls.
• Partners with the Data Architecture on Gold-layer design decisions, ensuring the semantic layer is architecturally sound, maintainable, and scalable as the platform grows.

Data Product Development

• Leads the development of reusable data products in Unity Catalog, building the data product catalog that enables self-service analytics discovery without ad-hoc engineering intervention.
• Ensures analytics requests enter the engineering pipeline with defined acceptance criteria and data product specifications before build work begins, in coordination with Informatics intake.
• Drives consistency and reuse across analytics delivery by replacing bespoke, one-off SQL derivation with governed, versioned metric views and data products.

Team Leadership & Development

• Develops a team culture oriented around the semantic layer discipline, which is distinct from both Data Engineering (pipeline and platform focused) and Analytics & BI (dashboard and report focused).
• Builds individual development plans and career frameworks for the team, defining clear growth paths within the Analytics Engineering track.
• Fosters a culture of standards adherence, documentation discipline, and reusability across all data product and metric view development.

Cross-Functional Partnership

• Partners with Analytics & BI to ensure metric views meet BI consumption requirements and that the Analytics & BI team builds on the semantic layer without re-deriving logic.
• Partners with Data Engineering to ensure Gold-layer tables and Silver-to-Gold promotion logic are aligned with the data engineering platform architecture.
• Partners with Data Governance to ensure metric view definitions are governed assets: ownership assigned, lineage tracked, and metadata standards enforced.
• Partners with Informatics to ensure analytics requests enter the build pipeline with defined scope and acceptance criteria.
• Partners with QA to support data layer validation of analytics outputs against governance-defined quality dimensions.

Required Qualifications

• 7+ years of progressive data engineering or analytics engineering experience, with at least 2 years in a people management or team lead capacity.
• Deep, hands-on expertise with Databricks:
  Unity Catalog, metric views, Delta Lake, medallion architecture, and Silver-to-Gold promotion logic in production environments.
• Demonstrated experience owning or building a semantic layer function: metric definitions, data product development, and the discipline of defining business metrics once for reuse across consumers.
• Clear understanding of analytics engineering as a distinct discipline from data engineering (pipelines, ingestion) and BI (dashboards, reports); able to articulate and hire to that distinction.
• Proficiency in SQL and…