Data Warehouse Architect 5

Data Warehouse Architect (Level 5) –
Solutions Architect

Required Skills (Must haves):

1. Minimum 5 years
experience in leading Data Architects and working with Enterprise Architects to
develop Data Landscape, Data Strategy, Data Architectural approaches using any
industry standard Architecture framework such as TOGAF 9, FEAF, DODAF etc. to
align data landscape with business, application and technology landscapes of an
enterprise to support the implementation of data-driven business strategy.

2. Minimum of 5 years
experience in developing Reference Architectures, Architecture Patterns
Library, conducting Architectural Reviews to identify exceptions, and managing
the architectural exceptions to ensure architectural integrity of Enterprise
Data Platform in a large enterprise.

3. Minimum of 3 years
experience in driving RFP process of selecting Data Platform Technologies,
partnering with vendors to codevelop innovative EDP capabilities, driving EDP
innovation, and promoting data-driven decision-making culture in the enterprise
through Communities of Practice etc. 

4. Must have a BS in Computer Science /
Data Science / Information Systems or a related CS degree

Most Preferred Skills
-Experience with Data Lake, Delta Lake, EDP, Data Warehousing, and Databricks

Other Preferred Skills and Job Description below

Key Programming Languages: SQL, Python, R,
Java

Other Technologies, Concepts and
Frameworks: TOGAF, Data Lake, Delta Lake, NoSQL DB, GraphDB, EDP, Data Warehousing,
Data Marts, Databricks, Operational Data Stores, Power BI, Tableau

Job Description:

Data Platform Architect / Datawarehouse
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 with the following track record:

1.      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.

2.      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.

3.      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.

4.      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. 

5.      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.

6.      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.

7.      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 / Datawarehouse
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, reliability, and alignment with
  business objectives.
• Oversee the implementation of modern data platform components,
  such as storage, streaming, and orchestration services, and ensure they
  function cohesively.
• Establish governance frameworks for data quality, security,
  metadata management, and compliance with organizational and regulatory
  requirements.
• Collaborate with engineering, analytics, security, and business
  teams to translate strategic needs into technical solutions and roadmap
  initiatives.
• Responsible for selection of appropriate hardware, software,
  tools and system lifecycle techniques for different components of data
  warehouse architecture including ETL, Metadata, data profiling software,
  performance monitoring, reporting and analytic tools.

Highly Desired:

· Desirable to have demonstrated
experience in Supporting the enterprise in ensuring that all the Data Engineering
efforts such as POCs, early implementations, and technology refresh of legacy
systems etc. are aligned to help the Data Engineering team stay focused on
systematically building and maturing the required technical and delivery
capabilities

· Desirable to have experience in
tracking the Architectural adherence of Data Engineering Products and Pipelines
to the Enterprise Architecture Standards and Best Practices and supporting the
Data Engineering team to systematically enhance their capability maturity in
delivering high-quality Data Engineering Products.