Data Engineer Lead / Data Warehouse...

Data Engineer Lead / Data Warehouse Architect - State of Michigan DHHS

About the Role:

We are seeking a Data Engineering Lead and Data Warehouse Architect at Level 3 for the State of Michigan DHHS project (MI DHHS 168059). This is a hybrid role based in Lansing, MI. The ideal candidate will excel in data modeling, Lakehouse and Medallion Architecture concepts, delivering robust data/ML products using an agile DevOps framework.

Key Responsibilities:

• Serve as the primary State of Michigan technology lead engaging with the UoM proof of concept team to support best practice adoption within the state hosting environment and provide training to DAV data engineers.
• Oversee the design, development, and maintenance of critical data pipelines for analytics and operational systems.
• Architect and optimize data infrastructure, including storage solutions, orchestration frameworks, and ETL/ELT processes.
• Ensure high standards of data quality, reliability, security, monitoring, validation, and governance.
• Lead and mentor junior data engineering staff while reviewing designs and code.
• Collaborate closely with data scientists, analysts, product teams, and business stakeholders to deliver impactful solutions.

Required Skills & Experience:

1. Data Pipeline Hydration (4+ years): Minimum 4 years of hands-on experience hydrating enterprise Data Lakes on Cloud Service Providers (AWS, Azure, or Google) from relational, semi-structured, and unstructured data sources by building robust Extract and Load pipelines.
2. Data Transformation & Product Delivery (4+ years): Experience transforming datasets in a Data Lake to deliver products with full traceability of attributes/features to Critical Data Elements in systems of record.
3. Data Governance & Security Enforcement (2+ years): Proven experience enforcing Data Sharing Policies so users see only the version of the product they are entitled to at subscription time.

Tech Stack Requirements:

• Languages/Frameworks: Python, R, Spark (PySpark), SQL.
• Architecture & Tools: Lakehouse/Medallion Architecture, Parquet format, Kafka, Kubernetes, Docker, DBT, Jupyter Notebooks, Terraform, DAGs/YAML.
• Cloud Services: AWS, Microsoft Azure, Google Cloud Platform (GCP), SageMaker, Databricks, Glue, Redshift Spectrum.
• Databases: NoSQL DB, Graph DB, Relational/Columnar stores.
• Data Quality & Governance: DataHub, Machine Learning integration.
• Visualization: Power BI (required), Tableau.

Educational Requirements:

Bachelor's Degree in Computer Science / Data Science / Information Systems with 4+ years designing and implementing data, analytics, and AI solutions for large enterprises on major cloud platforms.

Preferred Skills:

• Mastery of distributed data architectures essential for operational ML applications.
• Promoting Data Engineering Standards and Best Practices among developers.
• Implementing automated enforcement of Data Sharing Policies at the point of subscription using DevOps tools (GitHub/Jenkins/Terraform).