Data Warehouse Architect Hybrid

• Please include 2 professional references attached as a SEPERATE document. Must include their name, company they worked together at, title, phone number, and email

• Please note: travel outside of the weekly commute is required for this role: Occasionally traveling to Ann Arbor for in-person meetings. Nothing you need to attach to the submission for this, just make sure your candidates are aware and ok with this before submitting.

• Hybrid Work Schedule: Wednesday and Thursday are REQUIRED in-office days. This is NON-NEGOTIABLE and there is NO remote only option.

• Local candidates vs non-local: Open to candidates local to the Lansing Michigan area and those willing to relocate. Candidates that relocate must relocate before day one and be ready to be on-site from day one at their own expense (non-negotiable)

• Clear and concise written and verbal communication is crucial to success and productivity in this role

Data Engineering Lead excels in data modeling, Lakehouse and Medallion Architecture concepts, and data/ML products delivery using an agile DevOps framework with a mastery of distributed data architectures essential for data, analytical and operational ML applications with demonstrated proficiency in frameworks including Spark, Kafka, Kubernetes, SageMaker, DBT, Juniper Notebooks, Docker, SQL, and visualization tools such as Power BI/Tableau with the following track record:

Required Education: Bachelors Degree Required Skills:

• Minimum 4 years of hand-on experience is required in hydrating the enterprise Data Lake on a Cloud Service Provider (AWS, Azure, or Google) from relational, semi-structured and unstructured data sources by building Extract and Load Data Pipelines.

• Minimum of 4 years hands-on experience is required in Transforming the datasets in a Data Lake on a Cloud Service Provider to deliver Data Products with traceability of their attributes / features to the Critical Data Elements in the systems of record.

• Minimum of 2 years experience is required in enforcing Data Sharing Policies to Data Products to ensure that User get to see only the version of the Data Product the User is entitled to get access to at the time of subscription.

Preferred Skills:

• Proficiency desirable in DevOps tools including Github/Jenkins/Terraform/YAML/DAGs/Docker/Databricks/SageMaker etc. for building Data/ML pipelines to extract/store data from multiple sources to parquet destinations.

• Experience desirable in promoting Data Engineering Standards and Best Practices among the community of developers to influence the enterprise towards building scalable and supportable Data/ML Products delivery and their usage by the community of data, analytics, and ML product users.

• Experience desirable in implementing Data Governance and Data Security tools available on a CSP to automate the enforcement of Data Sharing Policies at the point of subscription of a Data Product by a subscriber.