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Pantherx Specialty LLC

Manager- Data Engineering

Pittsburgh, PA, United States

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Job details

Experience: 7+ years of progressive data engineering experience, with at least 2 years in a people management or team lead capacity.
Posting language: English
Posted: Nov 3, 2026

Original job description

7,000 Diseases - 500 Treatments - 1 Rare Pharmacy

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.

At PANTHERx, we know our employees are the driving force in what we do. We cultivate talent and encourage growth within PANTHERx so that our associates can continue to explore their interests and expand their careers. Guided by our mission to provide uncompromising quality every day, we continue our strategic growth to further reach those affected by rare 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, Data Engineering leads the Data Engineering track within the Data & Analytics organization, owning the foundational data platform: pipeline development, source system ingestion, Silver and Gold layer transformation logic, EDW design and operations, and the Databricks platform infrastructure. This role is responsible for building and leading a high-performing engineering team, maintaining the reliability and scalability of the data platform, and delivering the engineering foundation that the Analytics Engineering, Analytics & BI, and Partner Data Services tracks depend on.

Responsibilities

Platform Engineering & Architecture

Owns the data engineering platform: Databricks medallion architecture (Bronze, Silver, Gold layers), Delta Lake pipelines, source system ingestion patterns, and EDW design and operations.
Leads the evolution of ingestion patterns toward event-driven, low-latency architectures using Azure Event Hubs and Structured Streaming for high-SLA source systems.
Sets and enforces engineering standards for pipeline development, code quality, testing, documentation, and deployment across the Data Engineering track.
Partners with the Data Architecture on architecture decisions, ensuring new pipelines are designed to support governance-defined lineage capture, quality checkpoints, and observability requirements.

EDW Ownership & Operations

Owns the enterprise data warehouse as a production system: data model integrity, pipeline reliability, ingestion SLAs, and incident response.
Maintains and evolves EDW architecture, feed logic, and Gold harmonization rules, ensuring critical platform knowledge is documented and distributed across the team rather than concentrated in individuals.
Drives continuous improvement of platform reliability, latency, and cost efficiency across Bronze, Silver, and Gold layers.

Team Leadership & Development

Leads, develops, and retains Sr. Data Engineers and Data Engineers, building a team with deep Databricks expertise and clear ownership of critical platform capabilities.
Makes individual contributor hiring decisions in coordination with the Sr. Director; defines role profiles and hiring criteria for the Data Engineering track.
Builds individual development plans, conducts performance management, and fosters a team culture of engineering craft, documentation discipline, and reliability ownership.

Cross-Functional Partnership

Partners with Analytics Engineering to ensure Gold-layer table design and Silver-to-Gold promotion logic support metric view definitions and data product requirements.
Partners with the QA to embed validation checkpoints in pipeline architecture and support Tier 1 and Tier 2 pipeline test suite development.
Partners with Data Governance on pipeline architecture decisions that have lineage capture, metadata, and quality policy implications.
Partners with Partner Data Services on feed engineering work requiring new or modified pipeline development.

Required Qualifications

7+ years of progressive data engineering experience, with at least 2 years in a people management or team lead capacity.
Deep, hands-on expertise with Databricks: medallion architecture, Delta Lake, Unity Catalog, and production pipeline development and operations.
Demonstrated experience with enterprise data warehouse design, operations, and transformation logic in production environments.
Proficiency in PySpark, SQL, and Python; sufficient to review architecture decisions and code quality and mentor engineers across the team.
Proven experience with Azure cloud services: Azure Databricks, Azure Data Lake Storage, and Azure Event Hubs.
Strong communication and cross-functional collaboration skills; able to represent Data Engineering effectively with peer functions including QA, Governance, and Informatics.

Preferred Qualifications

Healthcare, specialty pharmacy, or regulated industry experience with exposure to clinical or operational data environments.
Experience with Structured Streaming and event-driven ingestion patterns (Azure Event Hubs, Kafka, or equivalent) in production environments.
Familiarity with data contract standards (ODCS or equivalent) and pipeline observability platforms (Monte Carlo, Datadog, or similar).
Experience partnering with data governance and QA functions to embed quality standards and lineage capture in pipeline architecture.
Familiarity with data catalog tooling (Atlan, Collibra, or equivalent) and metadata standards for governed data assets.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent experience.

Work Environment

This position works in a home office and professional office environment. When in-office this role routinely uses standard office equipment such as computers, phones, photocopiers, filing cabinets and fax machines, and communications via MS Teams.

Physical Demands

While performing the duties of this job, the employee is regularly required to sit, see, talk or hear. The employee frequently is required to stand; walk; use hands and fingers to handle or feel; and reach with hands and arms. Visual acuity is necessary for tasks such as reading and working with various forms of data on a screen. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions of the job.

Benefits:

Hybrid, remote and flexible on-site work schedules are available, based on the position. PANTHERx Rare Pharmacy also affords an excellent benefit package, including but not limited to medical, dental, vision, health savings and flexible spending accounts, 401K with employer matching, employer-paid life insurance and short/long term disability coverage, and an Employee Assistance Program! Generous paid time off is also available to all full-time employees, as well as limited paid time off for part-time employees. Of course we offer paid holidays too!

Equal Opportunity:

PANTHERx Rare Pharmacy is an equal opportunity employer, and does not discriminate in recruiting, hiring, promotions or any term or condition of employment based on race, age, religion, gender, ethnicity, sexual orientation, gender identity, disability, protected veteran's status, or any other characteristic protected by federal, state or local laws.

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More about this job

Skills

Data engineering
Patient care
Databricks
Microsoft Azure
Documentation
Data warehouse
Incident response
Process improvement
Team leadership
Performance management
Product comprehension
Unity
PySpark
SQL
Python
Communication skills
Collaboration
Apache Kafka
Datadog
Computer science
Office equipment
Microsoft Teams

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