Data Engineer Lead / Data Warehouse Architect

The role will lead the design and delivery of enterprise Data Lake, Lakehouse, Data/ML products and distributed data architectures using Agile/DevOps practices. The position requires strong hands-on experience with cloud data platforms, data modeling, data pipelines, Medallion Architecture and ML/data engineering technologies., * Databricks

• SageMaker
• Apache Spark / PySpark
• Apache Kafka
• Kubernetes
• SQL / NoSQL
• Graph Databases
• Python / R
• Terraform
• Docker
• YAML
• DAGs
• GitHub
• Jenkins
• dbt
• AWS / Azure / Google Cloud
• Parquet
• Power BI / Tableau
• AWS Glue
• Redshift Spectrum
• DataHub
• Machine Learning, * Design, develop and maintain enterprise data pipelines.
• Architect and optimize data infrastructure.
• Design storage solutions and orchestration frameworks.
• Develop ETL/ELT processes.
• Transform and prepare data for analytics and operational systems.
• Establish data quality, reliability and security standards.
• Implement validation and governance practices.
• Lead and mentor data engineering teams.
• Review technical designs and code.
• Collaborate with data scientists, analysts, product teams and business stakeholders.
• Support the State of Michigan technology environment.
• Work with the University of Michigan proof-of-concept team to promote data engineering best practices.
• Provide technical training and guidance to data engineers.

Preferred Skills:

• GitHub
• Jenkins
• Terraform
• YAML
• Docker
• DAGs
• Databricks
• SageMaker
• Data governance/security tools
• Data-sharing policy enforcement
• Data engineering standards and best practices
• Lakehouse/Medallion Architecture
• Data/ML pipeline development

• 4+ years hands-on experience building enterprise Data Lakes on AWS, Azure or Google Cloud.
• Experience ingesting relational, semi-structured and unstructured data.
• 4+ years transforming Data Lake datasets into data products.
• Experience maintaining traceability from data-product attributes/features back to critical data elements.
• 2+ years enforcing data-sharing policies and access controls for data products.
• 4+ years designing and implementing data, analytics and AI solutions for large enterprises.
• Strong Python / PySpark / SQL experience.
• Experience extracting data from relational databases, flat files, CSV, HTML and JSON sources.
• Experience implementing Lakehouse Medallion Architecture.
• Experience working with Parquet data formats.
• Experience designing Star Schema-based operational data stores.
• Power BI or Tableau experience., Important:The ideal candidate should be a hands-on senior Data Engineer/Architect, not just a data analyst. Strong experience with Cloud + Data Lake/Lakehouse + Python/PySpark/SQL + Medallion Architecture + Data Products + Data Governance is critical.Please send me your updated resume