Data Engineer Data Jobs in Michigan (NOW HIRING)

The Data Engineer will design, build, and maintain secure, scalable data pipelines, integrations, and data platforms that support CNS Healthcare’s clinical, operational, quality, and billing functions.  

What we are looking for:

CNS Healthcare, a leading provider of Mental Health Services in southeast Michigan is seeking a Data Engineer to join our dynamic and fast-paced team. The Data Engineer is responsible for enabling secure exchange of healthcare data, particularly involving State of Michigan CHAMPS systems for direct billing, while ensuring strict adherence to HIPAA and other regulatory requirements governing PHI/PII. 

Interested in learning more about this role? 

• Design, develop, and maintain scalable ETL/ELT pipelines to ingest, process, and transform healthcare data from EHRs, billing systems, quality systems, HR systems, and external partners.
• Ensure high data quality through validation, cleansing, reconciliation, observability, and monitoring processes.
• Support real-time and batch data workflows for clinical reporting, analytics, quality measures, CCBHC reporting, and billing operations.
• Build reusable data assets and pipelines that support operational reporting, revenue cycle analytics, quality bonus reporting, and executive dashboards.

• Support and extend the Proactive Solutions data platform and related reporting tools, including the Productivity Report and future data mart / analytics capabilities.
• Partner with Proactive Solutions, CNS IT leadership, and the data team to move the platform from development to production in a secure, supportable manner.
• Help define and implement production support practices, including deployment procedures, release management, environment configuration, monitoring, incident response, and issue escalation.
• Ensure the platform can be maintained by CNS over time through clear documentation, knowledge transfer, runbooks, and repeatable support processes.
• Design solutions that allow CNS to evolve toward a longer-term architecture without requiring unnecessary rebuilds.

• Support AI-assisted and agentic data engineering capabilities, including secure use of tools such as GitHub Copilot, Open Code, Azure OpenAI, OpenAI, Anthropic, or other approved enterprise AI services.
• Help implement secure BYOK/API-key patterns where appropriate, including credential management, access controls, audit logging, usage monitoring, and vendor compliance requirements.
• Collaborate with IT leadership and vendors to ensure that any AI-enabled coding, data query, or analytics capabilities are implemented in a HIPAA-compliant and security-approved manner.
• Assist in evaluating AI tool performance, cost, security posture, and operational fit for CNS’s data engineering and analytics workflows.
• Develop practical patterns for using AI safely to accelerate SQL development, pipeline generation, documentation, test generation, data quality checks, and troubleshooting.

• Build and maintain secure interfaces and integrations with Michigan CHAMPS (Community Health Automated Medicaid Processing System) for claims submission, eligibility verification, reconciliation, and related Medicaid workflows.
• Develop APIs and data exchange mechanisms using healthcare standards such as HL7, FHIR, and X12 EDI transactions for claims, remittance, and related processes.
• Ensure reliable, compliant data exchanges with payers, state systems, county partners, and partner organizations.
• Collaborate with clinical, billing, quality, finance, and compliance stakeholders to translate business and regulatory requirements into reliable data flows.

• Implement and maintain robust data protection controls to safeguard Protected Health Information (PHI) and Personally Identifiable Information (PII).
• Ensure compliance with HIPAA, HITECH, State of Michigan data security requirements, Medicaid requirements, and CNS internal security policies.
• Apply encryption, tokenization, access controls, audit logging, least-privilege access, secure secret management, and data masking techniques.
• Partner with compliance, security, and vendor teams on risk assessments, audits, vendor reviews, and AI governance requirements.
• Ensure sensitive data is not exposed to unapproved AI models, developer tools, repositories, logs, prompts, or third-party services.

• Design and maintain secure and scalable data models supporting clinical, financial, quality, operational, and executive reporting.
• Optimize data storage and retrieval across data warehouses, data marts, data lakes, cloud services, and operational databases.
• Support reporting tools and analytics platforms used for regulatory reporting, CCBHC metrics, quality bonus measures, productivity reporting, and revenue cycle management.
• Define standards for data models, naming conventions, lineage, metadata, data definitions, and source-to-target mapping.

• Manage and optimize data infrastructure across cloud and/or on-premises environments, ensuring reliability, scalability, performance, and cost-effectiveness.
• Monitor pipelines, integrations, AI-enabled services, and platform components for performance, failures, anomalies, and security events.
• Troubleshoot issues and implement improvements to enhance system resilience and reduce operational risk.
• Help design and validate backup, disaster recovery, and high availability approaches for critical data platform components.
• Support environment separation, including development, test, staging, and production patterns where appropriate.

• Collaborate with clinical, billing, finance, quality, compliance, IT, vendor, and executive stakeholders to understand data requirements and workflows.
• Document system architectures, data flows, integration points, AI/LLM configurations, security controls, runbooks, and operational support procedures.
• Provide technical expertise in healthcare data exchange, compliant AI adoption, and data engineering best practices.
• Train and mentor members of the data team on platform support, SQL development patterns, pipeline troubleshooting, source control practices, and safe use of AI-enabled development tools.

• Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, or related field, or equivalent professional experience.
• 10 years of experience in data engineering, data architecture, software engineering, analytics engineering, or a related technical role.
• Strong programming skills in Python, SQL, and/or Java/Scala.
• Experience with ETL/ELT tools and modern data platforms such as Azure Data Factory, SSIS, Databricks, dbt, Synapse, Fabric, or similar technologies.
• Knowledge of relational databases, NoSQL databases, data modeling techniques, and data warehouse / data mart design.
• Experience with source control, release management, deployment practices, and production support for data or software platforms.
• Ability to create clear technical documentation, runbooks, and support procedures for systems that must be maintained by internal teams.
• Proficiency with Power BI or similar data analytics application
• Ability to explain technical issues to technical and nontechnical internal and external stakeholders.