HealthPartners is hiring a Data Engineer to help expand and scale the data foundation that powers pharmacy analytics, reimbursement strategies, and critical business decisions. At HealthPartners, our mission is to make healthcare more affordable and accessible through smarter use of data. We are looking for an Engineer who thrives on solving complex data challenges and building reliable, scalable data solutions.
As part of the Care Delivery Pharmacy Data Team, you will design, build, and optimize data pipelines and products that transform pharmacy, claims, pricing, cost, and reimbursement data into trusted assets for analytics and decision-making. Collaborating closely with analysts, engineers, and business partners, you will improve data quality, automation, reliability, and accessibility while applying modern data engineering practices.
Your work will support initiatives including prescribing trend analysis, 340B program optimization, reimbursement analytics, payer contracting strategies, purchasing decisions, and executive reporting. By accelerating the integration and analysis of complex pharmacy data, you will help uncover opportunities, manage costs, and support informed decision-making across the organization. Reporting to the Senior Manager of Health Informatics, you will join a collaborative team consisting of one Senior Data Engineer and two Data Analysts.
We work using Agile practices and partner closely with pharmacy, informatics, finance, and operational leaders to deliver incremental value through iterative delivery, continuous improvement, and shared ownership. This role adds critical engineering capacity to support a growing portfolio of pharmacy analytics initiatives. Required
Bachelor's degree in Computer Science, Data Science, Social Science, Operations Research, Statistics, Applied Mathematics, Econometrics, or another quantitative field with four (4) years of experience in business analytics, data science, software development, data modeling, and/or data engineering, OR a Master's degree in Computer Science, Software Engineering, Mathematics, or a related field. Two (2) years of experience in a hands-on data engineering role.
Two (2) years of experience working with SQL and Python. Two (2) years of experience working with modern big data platforms such as: Azure Data Factory, Azure Synapse, PowerBI, Databricks, or similar. Knowledge of data modeling concepts and techniques, including star and snowflake schemas, denormalized data models, and third normal form (3NF).
Understanding of common data formats, including Parquet, CSV, and JSON. The following qualifications are considered critical to success in this role. Candidates should have substantial, demonstrated experience in these areas.
Knowledge of data processing methodologies, including batch processing, time-based partitioning, distributed processing, and real-time data streaming. Strong data profiling, analytical, and critical thinking skills, with the ability to identify patterns, trends, and opportunities within complex datasets. Experience with version control and CI/CD practices; Git experience preferred.
Self-motivated, curious, and creative, with the ability to work independently and take initiative. Strong verbal and written communication skills, with the ability to effectively partner with business stakeholders, end users, and technical teams. Demonstrated ability to collaborate effectively within diverse, cross-functional development and operations teams.
Experience working in Agile and Scrum-based development environments. Experience working in a public cloud infrastructure. Interest in advancing DataOps practices, including CI/CD, Infrastructure as Code (IaC), automated testing, and configuration management.
Knowledge of healthcare operations, healthcare data, and payer or care delivery processes. Proven ability to collaborate effectively with product managers, program managers, engineers, and business stakeholders. Strong analytical, problem-solving, and critical thinking skills.
Demonstrated ability to influence without direct authority and succeed in a fast-paced, evolving environment. Ideal candidates will have expertise in one or more of the following areas: Relational databases, including Oracle and Microsoft SQL Server. SQL and Oracle performance tuning, query optimization, stored procedures, and triggers.
CI/CD, continuous testing, and site reliability engineering (SRE) practices. Microsoft Azure technologies, including Azure Data Factory, Synapse Analytics, Purview, Databricks/Spark, Power BI, and Power Apps. Event streaming and real-time data integration tools such as Apache Kafka, Apache Flink, and Apache NiFi.
AI/ML Operations (MLOps) and the deployment, monitoring, and support of machine learning solutions. Hours/Location: M-F; core business hours This position offers a flexible hybrid work environment, with most of the work performed remotely. Team members are expected to be onsite approximately 1-2 times per month for collaboration, team meetings, and other business needs.
To support these periodic onsite requirements, preference will be given to local and regional candidates. Compensation Range: This position is available at an intermediate or senior level, depending on qualifications. Intermediate Data Engineer – $42.15 - $63.23 hourly Senior Data Engineer - $51.85 - $77.77 hourly Compensation is based on the level and requirements of the role.
Pay within our ranges may also be determined by education, experience, knowledge, skills, location, and abilities as well as internal equity. Hired candidates may be eligible to receive additional compensation based on role (e.g., shift differential, bonus, sales incentive, productivity pay, etc.).
All team members must champion and model our values of partnership, curiosity, compassion, integrity, and excellence, and must contribute to a culture of continuous learning. Collaborate with stakeholders, data scientists and analysts to frame problems, clean, and integrate data, and determine the best way to provision that data on demand. Collaborate with other developers to design technology solutions that achieve measurable results at scale.
Collaborate with data scientists to train, develop, and operationalize learning models. Build, design, and develop scalable, efficient data pipeline processes to manage data ingestion, cleansing, transformation, integration, and validation required to provide access to prepared data sets for analysts and data scientists. Evaluate and recommend technology and frameworks for building cross product data assets to optimize for flexibility, long-term viability, and time to market.
Guide, mentor, influence, and adopt a cloud-first modern data architectural direction and consistently adopt the associated standards and best practices. Anticipate the need for data governance and work with designated committees to ensure that data modeling and data handling procedures are compliant with applicable laws and policies across data pipeline. Leads root cause analysis in response to detected problems/anomalies to identify the reason for alerts and implement advanced solutions that prevent recurring points of failure.
Applies in-depth knowledge of the business to design a data model that is appropriate for the project and translates business requirements into design specification documents to model the flow and storage of data across multiple data pipelines. Identifies multiple, complex data sources and builds advanced code to extract raw data from identified upstream sources using query languages, tools, or machine learning algorithms, while assuring accuracy, validity, and reliability of the data across the pipeline.
Formulate techniques for quality data collection to ensure adequacy, accuracy, and legitimacy of data. Perform unit tests and conduct reviews with other team members to make sure your code is rigorously designed, elegantly coded, and effectively tuned for performance. Participate in code reviews and champion the adoption of best practices.
Support the data environment by releasing new features, resolving issues for users, and working with other technology teams. Monitor and analyze information and data systems and evaluate their performance to discover ways of enhancing them (such as new technologies and upgrades). Applies in-depth knowledge of the business to design a data model that is appropriate for the project and translates business requirements into design specification documents to model the flow and storage of data across multiple data pipelines.
Data engineers perform other duties as required, to meet team sprint goals.