Data Warehouse Architect; Hybrid Onsite

Data Warehouse Architect (Hybrid Onsite)

Location:

Lansing, MI

Duration: 12 months

Interview Type:
Virtual & In-Person

Note:

Interview Process. Virtual and in-person. First round will be virtual, second round will be on-site. Mix of technical and soft skills. For the virtual round it will be a 60-minute Virtual Interview via MS Teams (video required). Candidates should join from a laptop and be prepared to share their screen if requested. A screenshot photo of candidate will be required for any interviews as well as a vendor present at beginning of virtual interview to validate candidate.

Assignment Duration: 1yr+ contract dependent on business need

Hybrid Work Schedule (non-negotiable):
There is NO remote-only option. Wednesdays and Thursdays are required on-site days (non-negotiable)

Open to local candidates or those willing to relocate BUT they must relocate from day one AND they must come on-site for 2nd round interview. Candidates not willing to do this will not be considered.

Travel Requirement:
Will sometimes drive from downtown Lansing to University of Michigan Campus. 20% travel. Candidates MUST be willing to do this.

References:
Candidates must provide 2 Professional references. Please attach them to the bid in a separate document.

Candidates MUST meet all these requirements from the beginning

Job Description:

Data Platform Architect / Data warehouse Architect (Level
5) excels in tracking emerging industry capabilities for modern Enterprise Data Platform (EDP), developing target state Data Platform Architecture, and architecting Data, Analytics, and ML Products that are aligned with the enterprise data strategy, data landscape, data skills, data security, and data sharing needs to support the realization of enterprise Business Strategy outcomes.

Must have a BS in Computer Science / Data Science / Information Systems or a related CS degree with an overall 5+ years of experience in developing Enterprise Data Technology Strategies, articulating the use of the Data Engineering Delivery Methodologies, building the Data Engineering Standards & Best Practices to ensure alignment of Data/Analytics/ML Products with the Target State Architecture, and promoting the use of the Data Engineering products in the community of Users using industry standard Enterprise Architecture frameworks such as TOGAF, FEAF, DODAF etc.

Must have Demonstrated expertise in driving innovation related to modern data technology platforms through conducting Proofs of Concepts, Codathon, and Co-development with technology vendors, to fully comprehend the business capabilities feasible from emerging technologies to design effective Proofs of Concepts and lead the execution of POCs in the enterprise to support technology decision making in the enterprise.

Must have experience in the full technology stack within an Enterprise Data Platform offering of any CSP to help an enterprise set up the initial fully functioning instance of an EDP containing all the required tools to enable the Data Engineering team in conducting Proofs of Concepts and operationalizing the Product Environment for the delivery of Data Engineering products including Data/Analytics/ML pipelines.

Must have demonstrated experience in driving the procurement process (RFI/RFP etc.) in a large enterprise to select the Cloud Service Provider vendor for building and hosting the EDP.

Must have demonstrated experience in architecting Data Services Portfolio and Data Products that are aligned with the industry best practices and internal data engineering capabilities.

Must have demonstrated expertise in baking in the Data Governance standards and best practices into the development and usage of the Data Engineering Products including the Data/Analytics/ML pipelines and Data/Analytics/ML Products.

Must have demonstrated experience in enforcing the adherence to the implementation of Data Security Standards and Best Practices into the Data Engineering Products including Data/Analytics/ML Products and Data Pipelines to minimize data security vulnerabilities.

Roles and Responsibilities:

Data Platform Architect / Data warehouse Architect (Level
5) excels in tracking emerging industry capabilities for modern Data Platforms, developing target state Data Platform Architecture, and architecting Data, Analytics, and ML Products that are aligned with the enterprise data strategy, data landscape, data skills, data security, and data sharing needs to support the realization of enterprise Business Strategy outcomes.

Designs, implements, and supports MDHHS data warehouse and analytics platform modernization initiatives. Recommends and leads State of Michigan teams in adopting emerging cloud-based data services, analytical tools, and other modern technologies. Oversees the organizational sustainability of data warehouse and data analytics process improvement. The Data Platform Architect’s responsibilities include:

Design and maintain the overall architecture for enterprise data platforms, ensuring scalability,…