Ml Infrastructure Engineer Jobs in Michigan (NOW HIRING)

If you are unable to complete this application due to a disability, contact this employer to ask for an accommodation or an alternative application process.

Full Time Regular St. Clair Shores, MI, US

Fisher Dynamics is the automotive industry’s premier supplier of safety – critical seat structures and mechanisms. Steeped in a tradition of excellence, and rooted in automotive innovation, the Fisher story is filled with automotive manufacturing milestones. We bring design, engineering, and manufacturing vehicle seating systems to a new level with innovative thinking. We’re about cutting edge ideas. We have created an environment that encourages an uninterrupted flow of revolutionary concepts and unique ideas.

The Data Engineer - II, will architect and build the data foundation that powers Fisher Dynamics' custom ERP platform and its embedded AI/LLM capabilities. They will design robust, scalable data pipelines that extract, transform, and load data from Plex and operational sources into the new ERP system. Along with building real-time data streaming systems that feed machine learning models with clean, accurate, and timely data for intelligent ERP features; this position will establish data governance, quality standards, and compliance frameworks that ensure data integrity, security, and regulatory adherence. Along with collaborating with ML/AI engineers, SW engineers, and business stakeholders to deliver a data-driven, AI-native ERP platform.

Candidates MUST be local to the Metro Detroit Area. Relocation is not available.

This role does not provide immigration sponsorship. Candidates must be legally authorized to work in the US without requiring sponsorship now or in the future.

• Design and build scalable, fault-tolerant data pipelines for ERP data ingestion, transformation, and loading.
• Implement ETL/ELT processes that migrate legacy ERP data into the new ERP system with data validation and quality checks.
• Build real-time data streaming pipelines using Kafka, Spark, or similar technologies for continuous data flow.
• Develop batch processing jobs for scheduled data transformations and aggregations.
• Ensure data pipelines handle large volumes, complex transformations, and operational resilience.
• Data Governance & Quality Management
  • Establish data governance policies, standards, and procedures for ERP data.
  • Implement data quality monitoring and validation frameworks to ensure data accuracy and consistency.
  • Build data profiling, cleansing, and validation tools to maintain high-quality data.
  • Document data lineage, metadata, and data dictionaries for transparency and compliance.
  • Monitor data quality metrics and SLAs; alert on data issues and drive resolution.
• Design and implement cloud-based data architecture on AWS, GCP, or Azure (data warehouses, data lakes, etc.).
• Build and optimize data storage solutions for ERP transactional and analytical data.
• Implement data security, encryption, and access controls for sensitive financial and operational data.
• Optimize data infrastructure for performance, cost, and scalability.
• Monitor and troubleshoot data infrastructure issues.
• Collaborate with ML/AI engineers to understand feature requirements and data needs for AI models.
• Design and build feature stores and feature pipelines that deliver data for model training and inference.
• Engineer features from raw ERP data (transactions, master data, time-series) optimized for ML models.
• Build real-time feature serving infrastructure for low-latency model inference.
• Support ML/AI engineers with exploratory data analysis and data debugging.
• Data Migration & Integration
  • Lead data migration from Plex to new ERP system with data validation and reconciliation.
  • Build integrations with external data sources (suppliers, customers, market data) into ERP.
  • Implement data synchronization and consistency checks between source and target systems.
  • Manage historical data and archive strategies.
  • Support data cutover activities and validation.
• Performance Optimization & Troubleshooting
  • Monitor and optimize data pipeline performance, query efficiency, and data infrastructure.
  • Identify and resolve data bottlenecks and performance issues.
  • Build monitoring and alerting systems for data pipeline health.
  • Conduct load testing and capacity planning for data infrastructure.

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Bachelor's degree in Computer Science, Data Science, Engineering, or related field.

Master's degree preferred.

4-6 years of professional data engineering experience building production data systems.

Demonstrated experience designing and implementing large-scale ETL/ELT pipelines and data architectures required.

Experience with ERP system data integration or data warehousing strongly preferred.

Advanced proficiency in Python, Scala, Java, or similar data engineering languages.

Expert-level SQL and relational/dimensional database design.

Strong experience with data pipeline orchestration tools (Airflow, Prefect, Dagster).

Expertise in cloud data platforms (AWS Redshift/S3, Google BigQuery, Azure Data Lake).

Experience with big data technologies (Spark, Hadoop, Kafka, Flink).

Knowledge of data warehousing, data lakes, and data architecture patterns.

Strong understanding of ETL/ELT patterns, data transformation, and data quality.

Experience with version control (Git) and data pipeline version management.

Proficiency with containerization (Docker) and orchestration platforms.

Understanding of data governance, security, and compliance requirements.

Experience with feature stores and ML data pipelines is preferred.

Familiarity with ERP systems and business data models is preferred.

Strong problem-solving and debugging skills.

Excellent communication and ability to collaborate with data scientists and engineers.

Working environment and physical requirements of this position are those typical of an office setting and manufacturing environment. Position requires collaboration with technical teams and business stakeholders.