Senior Data Engineer - Scheduling & Decisions Systems

TAG - The Aspen Group•Chicago, IL

7d•$129,000 - $152,000•Onsite

About The Position

The Aspen Group (TAG) is one of the largest and most trusted retail healthcare business support organizations in the U.S., supporting over 23,000 healthcare professionals and team members at more than 1,150 locations across 48 states. Our five supported healthcare practices operate under the brands Aspen Dental, ClearChoice, WellNow, Chapter Aesthetic Studio, and Lovet. We’re committed to enabling healthcare professionals to focus on patient care while we handle the business operations that support them. As part of our continued investment in data-driven innovation, we are looking for a Senior Data Engineer to join our growing team. In this high-impact role, you will architect and build the data backbone that powers our next-generation scheduling platforms and patient demand forecasting models. You will move beyond traditional reporting to build "intelligence pipelines"—systems that ingest real-time operational data, feed advanced optimization algorithms, and write actionable insights back into clinical workflows. If you are passionate about using data to reduce patient wait times, optimize provider utilization, and ensure the right resources are available at the right time, this is the role for you.

Requirements

Experience: 5+ years of Data Engineering experience with a focus on Python and complex SQL.
Cloud Data Platform Mastery: Deep experience with AWS (Glue, Lambda, Kinesis), Azure (Data Factory, Synapse), or GCP (Dataflow, BigQuery).
Workflow Orchestration: Advanced proficiency with tools like Apache Airflow, Prefect, or Dagster.
Data Modeling: Experience designing dimensional models (Star Schema) and "One Big Table" structures for analytical performance.
Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or related technical field.

Nice To Haves

Experience with scheduling, optimization algorithms, or decision-support systems.
Forecasting Knowledge: Familiarity with time-series data preparation (handling seasonality, lag features, moving averages).
Familiarity with responsible AI practices and governance.

Responsibilities

Hybrid Pipeline Architecture (Batch & Streaming) Design event-driven pipelines that ingest live patient interactions to power real-time inference to be able to calculate various propensity scores depending on the stage of patient journey
Batch Processing: Maintain scalable batch processes for time-series analysis and other advanced statistical analysis as well as ML/LLM models that require heavy historical data aggregation and feature engineering
Patient Clustering: Build pipelines that aggregate clinical and behavioral attributes to support unsupervised learning (clustering) for patient segmentation which then might be used for different business use cases from scheduling to marketing
Scheduling Optimization: You will transform raw availability data into clean inputs for linear programming and constraint optimization solvers. Partner with cross-functional stakeholders to translate business requirements into technical specifications for ML solutions.
System Architecture & Observability Collaborate with Data Scientists and Operations Researchers to deploy forecasting and optimization models into production.
Build Feature Stores that serve consistent features to both training and inference environments.
Ensure the "reverse ETL" of model outputs—writing optimized schedules and recommended appointment slots back into operational systems for front-line staff to use.
Collaboration & Mentorship Create clear, comprehensive documentation and support guides for newly implemented tools.
Provide technical guidance and mentorship to junior engineers and data scientists.
Stay current with advances in machine learning, data engineering, and software development, implementing industry best practices for reliability and maintainability.