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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - Scheduling & Decisions Systems - **Company:** Aspen Dental Management, Inc. - **Location:** Chicago, IL, United States - **Experience:** Expert - **Salary:** $129,000.0 - $152,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Batch Processing, BigTable, BigQuery, Cluster Analysis, Information Engineering, Extract Transform Load (ETL), Data Normalization, Data Flow Control, Python (Programming Language), Linear Programming, Machine Learning, Software Engineering, SQL Databases, Data Streaming, Systems Architecture, Workflow Management Systems, Google Cloud, Cloud Platform System, Feature Engineering, Delivery Pipeline, Large Language Models, Information Technology, Optimization Algorithms, Star Schema, Operational Systems, Data Pipelines, Unsupervised Learning - **Published:** August 15, 2026 - **Apply:** https://www.dice.com/job-detail/6695a9e3-7b4a-4fb7-b507-39881d486d85 ## About the Role * 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 Google Cloud Platform (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. * 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. ## Description 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Making Data Warehouses fast. 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