Data Engineer for Pharmacy Analytics & Scalable Pipelines

HealthPartners; BLOOMINGTON (MN); Hybrid; USD 88,000 - 132,000; Full time

Job summary

HealthPartners is seeking a Data Engineer to expand the data foundation powering pharmacy analytics, reimbursement strategies, and critical business decisions.

You will design, build, and optimize data pipelines transforming pharmacy, claims, pricing, and reimbursement data into trusted analytics assets; collaborate with analysts, engineers, and business partners to improve data quality and accessibility.

Qualifications

• Bachelor's degree in a quantitative field with 4+ years in data analytics, data engineering, or related areas.
• 2+ years in a hands-on data engineering role.
• 2+ years with SQL and Python.
• 2+ years with Azure Data Factory, Azure Synapse, Power BI, Databricks, or similar.
• Knowledge of star/snowflake schemas, 3NF, Parquet/CSV/JSON formats.
• Experience with data processing: batch, streaming, partitioning.

Responsibilities

• Design, build, and optimize data pipelines for pharmacy, pricing, and reimbursement data.
• Collaborate with analysts, engineers and business partners to improve data quality and accessibility.
• Develop scalable data products and maintain CI/CD and Git-based workflows.
• Support analytics initiatives including prescribing trends, 340B, and payer contracting analysis.
• Lead data modeling efforts and ensure governance across pipelines.
• Participate in Agile/Scrum practices and cross-functional collaboration.

Skills

SQL

Python

Data Modeling

Big Data

Analytics

CI/CD

Communication

Cloud

Education

Bachelor's degree in CS/Math/Data Science

Master's degree in CS/Data Science

Tools

Azure Data Factory

Azure Synapse

Databricks

Power BI

Git

Job description

HealthPartners is seeking a Data Engineer to expand the data foundation powering pharmacy analytics, reimbursement strategies, and critical business decisions.

You will design, build, and optimize data pipelines transforming pharmacy, claims, pricing, and reimbursement data into trusted analytics assets; collaborate with analysts, engineers, and business partners to improve data quality and accessibility.