A cover letter is required for consideration for this position and should be attached as the first page of your resume. The cover letter should address your specific interest in the position and outline skills and experience that directly relate to this position.
The Supply Chain Services Data Engineer is responsible for designing, constructing, migrating, and maintaining robust, scalable data models and data warehousing solutions to deliver comprehensive, high-quality supply chain data across University of Michigan Health. This position reports through the Innovation Office and is dedicated to supply chain solutions.
Working within an established modern analytics-as-code stack (PostgreSQL, dbt, Prefect, Python, Git/GitLab CI) and integrating data from diverse sources (SQL databases, APIs, flat files, SFTP feeds), this role empowers analytics, reporting, and advanced AI/ML strategies used for standalone supply chain insights or integrated with clinical data to support a clinically integrated supply chain. The Data Engineer focuses on building and maintaining production-grade, well-documented data pipelines and repositories.
The Data Engineer works collaboratively with application developers, analytics teams, solution architects, and supply chain stakeholders to ensure data accuracy, performance, security, and accessibility, providing the critical infrastructure that supports patient care, operational efficiency, cost containment, and organizational strategy. This role is ideal for a technically skilled data professional passionate about designing systems that transform complex data into actionable insights for both clinical and supply chain excellence.
NOTE: A cover letter is required for this application. In your cover letter, please address how you meet the required qualifications and your interest in the position.
Data Engineering, Modeling & Integration
- Design, construct, migrate, and maintain scalable, well-documented relational data warehouses and analytics platforms focused on supply chain domains (procurement, inventory, distribution, contracts, finance) across multiple enterprise sites.
- Develop and refine comprehensive data models (conceptual, logical, physical) and transformation layers (staging, intermediate, marts) bridging raw data to business processes and end-user applications.
- Build, monitor, and maintain resilient ELT pipelines and orchestration flows using Prefect and Python, ensuring reliable batch data ingestion and transformation from internal and external sources across multiple enterprise sites and ERP systems.
- Collaborate with IT and data governance teams to facilitate consistent and accurate ingestion of data feeds from vendors, third-party tools, clinical systems, and enterprise sources - including regional site data (e.g., via SFTP) - complying with institutional governance standards.
- Lead onboarding and integration of new data sources, ensuring architecture compatibility and maintaining best practices in data lineage and quality.
- Support Master Data Management (MDM) initiatives within supply chain services, including golden-record strategies across a multi-ERP environment.
- Contribute to cross-departmental data unification efforts, enabling coherent data feeds across supply chain, pharmacy, finance, and other health system domains.
Performance, Security & Governance
- Optimize queries, indexing strategies, caching, and transformation models to support high-performance analytics and dashboarding tools.
- Implement scalable data validation, exception handling, and auditing processes for critical applications, using automated testing and task-level monitoring.
- Maintain comprehensive technical documentation and metadata for all architecture, processes, and models - including schema definitions, documentation files, and institutional governance artifacts - adhering to regulatory requirements such as HIPAA.
- Enforce robust security practices, PHI/PII protection, HIPAA compliance, row-level security, and least-privilege access across all database environments.
- Support and document continuous improvement initiatives - incorporating AI/ML advances (such as natural language processing and embedded AI) to boost performance, security, accessibility, and functionality of supply chain data solutions.
Strategic Partnership, Innovation & Education
- Collaborate with supply chain stakeholders, Solutions Managers, and Value Analysis teams to design data infrastructure supporting clinical decision-making, utilization reviews, and cost savings initiatives.
- Co-develop next-generation supply chain analytics leveraging predictive modeling, semantic models, AI, and natural language processing for advanced projects (e.g., semantic item classification, opportunity identification, intelligent routing, contract data extraction).
- Support migration and modernization efforts, integrating fragmented or siloed systems and legacy workflows (legacy ETL, manual extracts) into unified, code-versioned analytics architectures.
- Deliver consultative expertise, training, and support for supply chain and analytics teams adopting modern analytics-as-code and AI/ML tooling.
- Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a combination of degree and relevant work experience.
- 4-5 years of hands-on experience in data architecture, data modeling, data warehousing, or data engineering (preferably in healthcare or supply chain domains).
- Proven expertise working across multiple database platforms (e.g., PostgreSQL, SQL Server, Oracle) in a heterogeneous environment.
- Experience with a workflow orchestration tool such as Prefect, Airflow, or Dagster for building and managing data pipelines.
- Strong Python proficiency for data engineering, pipeline development, and automation.
- Proficiency in modern data modeling, normalization, ELT patterns, and relational database design.
- Experience with Git-based version control and CI/CD workflows (GitLab CI or equivalent).
- Demonstrated proficiency with AI and experience working with agentic coding tools (e.g., Cursor, OpenAI Codex, Claude Code, Anti-Gravity) to accelerate development workflows.
- Excellent analytical, problem-solving, and communication skills.
- Ability to work independently and exercise sound technical judgment on complex assignments.
- Master's degree in a related field (e.g., Information Systems, Health Informatics, Data Engineering, Supply Chain Management).
- Direct experience supporting healthcare supply chain analytics, data work
