Data Warehouse Architect

Top 3 skills needed: 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 Minimum of 5 years experience in developing Reference Architectures, Architecture Patterns Library, conducting Architectural Reviews to identify exception, and managing the architectural exceptions to ensure architectural integrity of Enterprise Data Platform in a large enterprise 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. Minimum years of experience required: See above. Key Programming Languages: SQL, Python, R, Java Other Technologies, Concepts and Frameworks: TOGAF, Data Lake, Delta Lake, NoSQL DB, GraphDB, EDP, Datawarehousing, Data Marts, Operational Data Stores, Visualization 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: Must have a BS in Computer Science / Data Science / Information Systems 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, Codathons, 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 a 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 / 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 innovation 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.