Data Engineer

• Own architecture and technical delivery for a healthcare and pharmacy benefit management data platform expansion
• Architect ingestion pipelines for X12 EDI (835, 837, 832, 810) and MTF files using Azure Data Factory and Databricks
• Design FHIR-based ingestion patterns with incremental sync, throttling, and retry handling
• Define canonical data models for referral, clinical document, and provider-administered drug data
• Extend the metadata-driven ingestion framework to support new payload types without platform redesign
• Architect HL7 ADT / SIU linkage supporting 340B eligibility determination
• Design clinical document storage, indexing, and retrieval architecture for CCD / C-CDA
• Establish trading partner onboarding and configuration patterns
• Guide reporting and analytics data model design for BI consumption
• Partner with client stakeholders and vendor teams on integration points
• Work with client stakeholders and distributed delivery teams from initial definition through final delivery

Data Architect

• Design, develop, and maintain scalable data ingestion and curation pipelines from diverse sources
• Standardize, improve the quality of, and optimize data for analytical use
• Build robust data pipelines using Python, SQL, and DBT/Dataform
• Contribute to seamless end-to-end development and reliable data flow from source to insight
• Build and manage GCP data platforms using BigQuery, Dataflow, Pub/Sub, and Cloud Functions
• Implement data governance policies, access controls, and cloud security best practices
• Manage data workflows and cloud infrastructure using Astronomer and Terraform
• Monitor and improve pipeline and storage performance, scalability, efficiency, resource utilization, and cost-effectiveness
• Collaborate with data architects, application architects, service owners, and cross-functional teams on best practices, design patterns, and frameworks
• Automate data platform processes to improve reliability, data quality, and operational efficiency
• Communicate complex technical decisions to technical and non-technical stakeholders
• Translate business requirements into optimized data asset designs and efficient code
• Document data engineering processes and promote knowledge sharing
• Continuously evaluate industry trends and emerging technologies to improve the data platform

Full Stack Data Engineer

• Design, build, and maintain scalable ETL/ELT pipelines for ingesting, transforming, and delivering data into Google BigQuery and other cloud data platforms
• Develop, document, and maintain RESTful/GraphQL APIs for internal applications, dashboards, and third-party consumers
• Design and manage a semantic data layer that translates raw data into consistent, business-friendly metrics and definitions
• Implement and maintain a data catalog/knowledge catalog for data discoverability, lineage tracking, and metadata management
• Optimize BigQuery performance through partitioning, clustering, query tuning, and cost-management practices
• Build data quality checks, monitoring, and alerting to ensure pipeline reliability and data trustworthiness
• Collaborate with analytics, product, and engineering teams to gather requirements and create scalable data models and API contracts
• Enforce data governance, security, and access-control standards, including PII handling, role-based access, and encryption
• Write clean, well-tested, maintainable Python/SQL and backend API code
• Participate in code reviews, architecture discussions, and CI/CD pipeline development

• Develop analytical data products using streaming and batch ingestion patterns on Google Cloud Platform
• Build data pipelines to monitor data quality and the performance of analytical models and agentic solutions
• Maintain data platform infrastructure using Terraform and deliver code through CI/CD
• Collaborate with data analytics stakeholders to streamline data acquisition, processing, and presentation
• Implement an enterprise data governance model covering data protection, sharing, reuse, quality, and standards
• Enhance and maintain DevOps capabilities of the data platform
• Optimize data solutions for performance, security, vulnerability reduction, cost, and reliability
• Work in an agile product team using TDD, continuous integration, and continuous deployment
• Address code quality issues using SonarQube, Checkmarx, Fossa, and Cycode
• Perform data mapping and lineage activities and document information flows
• Monitor production pipelines and provide production support according to SLAs
• Analyze connected vehicle data to support new product development and production vehicle improvements
• Provide visibility into data quality, vehicle, and feature issues and work with business owners to resolve them
• Collaborate cross-functionally, translate technical work for stakeholders, and advocate for well-designed solutions
• Mentor junior data engineering team members
• Stay current with data engineering practices and contribute to the company’s technical direction

• Provide technical and architectural leadership within the data engineering organization
• Develop and drive the long-term vision for enterprise data platforms and architecture
• Formulate and implement data platform strategy across big data, real-time, transaction/operational, and governance domains
• Oversee architecture, design, and development of scalable data platforms enabling self-service use cases and AI success
• Ensure production success, establish FinOps discipline, and optimize economics through forecasting, runtime analysis, and showback
• Communicate data technologies, strategies, roadmaps, and progress to executive leadership and stakeholders
• Monitor industry trends and translate emerging technologies into actionable initiatives
• Build a strong engineering organization and foster engineering discipline through leadership and mentoring
• Collaborate with data and engineering teams on technology and architecture strategy and decisions
• Contribute to critical system design, implementation, and operational success
• Formulate technology and architecture decision frameworks
• Develop business-process expertise to align technology with business outcomes
• Work with engineering leadership to execute company technical and business strategy
• Conduct impact analyses across multiple applications and systems
• Perform technical due diligence and architectural assessments of third-party technologies and solutions
• Experiment, test ideas, validate assumptions, and recommend solutions
• Document standards, best practices, and engineering artifacts
• Debug critical production problems and execute solutions across applications and systems
• Lead continuous learning and process improvement
• Participate in hiring and mentor team members

Director, Data Platform

• Design, build, and scale robust batch and streaming data pipelines on Google Cloud Platform to process large volumes of finance data
• Develop analytical data products using data warehouse principles, data modeling, and best practices
• Maintain and enhance platform infrastructure using Terraform and Infrastructure as Code
• Develop, evaluate, and deploy code through CI/CD pipelines
• Partner with data analytics stakeholders to optimize data acquisition, processing, and presentation workflows
• Implement enterprise data governance models, data protection, data sharing, standards, quality monitoring, and data lineage documentation
• Write clean, reliable code using Test-Driven Development and address security vulnerabilities and code quality issues
• Optimize pipelines, infrastructure, and data products for performance, security, reliability, and cost efficiency
• Monitor production pipelines and provide timely production support in accordance with established SLAs
• Stay current on modern data engineering practices, contribute to technical direction, and build finance data domain expertise

• Design, develop, test, and deploy the ISDP knowledge graph from the domain event store to Production using cloud-native data pipelines
• Model and evolve graph entities and relationships as new data sources are onboarded
• Design, build, and operate the MCP server exposing the ISDP graph layer and event store as tools to consumers
• Provide GQL graph query, event-store query, and schema/DDL discovery capabilities
• Define tool contracts, context, and guardrails so agents produce grounded, accurate, non-hallucinated responses
• Ensure low-latency, secure, and cost-efficient serving for interactive and batch agent workloads
• Own monitoring and observability for the graph layer and MCP server, including data freshness, pipeline health, query latency/cost, tool-call success rates, and answer quality
• Instrument SLOs, dashboards, alerting, and tracing; drive incident response and continuous reliability improvements
• Partner with Data Engineers and Application Data Source Owners across Product Development, Manufacturing, Quality, and Supply Chain
• Ingest and validate source data into ISDP
• Establish data contracts, schema validation, and quality checks
• Support source owners through onboarding, mapping to the ISDP logical model, and troubleshooting
• Contribute to data governance, cataloging, and lineage for the graph and its sources

Data Engineer jobs in Michigan

Data Architect

Full Stack Data Engineer

Data Engineer

Data Engineer

Director, Data Platform

Full Stack Data Engineer

AI Engineer, Industrial Systems Data Platform