Data Warehouse Architect

KēSTA I.T.•Lansing, MI

2d•Hybrid

About The Position

KēSTA I.T. is actively seeking an experienced Data Warehouse Architect to lead the architecture and modernization of enterprise data platforms, data warehouses, and analytics environments. This role will develop target-state data platform architectures, establish enterprise data strategies, and guide the design of Data, Analytics, and Machine Learning products aligned with business objectives, data governance, security, and data-sharing requirements. The ideal candidate will bring extensive experience in enterprise architecture, modern data technologies, cloud-based data platforms, and data engineering practices, along with a proven ability to lead architectural initiatives, evaluate emerging technologies, and drive large-scale technology decisions.

Requirements

Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field.
5+ years of experience leading Data Architects and collaborating with Enterprise Architects to develop enterprise data landscapes, data strategies, and data architecture approaches.
5+ years of experience applying enterprise architecture frameworks such as TOGAF, FEAF, DODAF, or comparable frameworks.
5+ years of experience developing Reference Architectures and Architecture Patterns Libraries.
5+ years of experience conducting architectural reviews, identifying architectural exceptions, and managing exception resolution within large enterprise environments.
5+ years of experience developing enterprise data technology strategies, data engineering methodologies, standards, and best practices.
3+ years of experience leading RFI/RFP processes for selecting data platform technologies and cloud-based data platform solutions.
3+ years of experience partnering with technology vendors to co-develop innovative enterprise data platform capabilities.
Demonstrated experience driving data platform innovation through Proofs of Concept, technical evaluations, codathons, or vendor co-development initiatives.
Demonstrated experience with the technology stack required to establish and operate an enterprise data platform within a cloud service provider environment.
Experience architecting Data Services Portfolios and Data Products aligned with enterprise strategies and data engineering capabilities.
Demonstrated experience integrating data governance standards and practices into data engineering products, pipelines, and Data/Analytics/ML solutions.
Demonstrated experience implementing and enforcing data security standards and best practices across data platforms, pipelines, and analytical products.
Strong understanding of enterprise data architecture, data warehousing, data engineering, analytics, and modern data platform concepts.
Strong technical leadership, communication, collaboration, analytical, and problem-solving skills.

Nice To Haves

Experience with Data Lakes and Delta Lake architectures.
Experience with Enterprise Data Platforms (EDP).
Experience with enterprise data warehousing and data marts.
Experience with Databricks.
Experience with NoSQL databases and GraphDB technologies.
Experience with Operational Data Stores (ODS).
Experience with Power BI and Tableau.
Proficiency with SQL, Python, R, and/or Java.
Experience supporting enterprise Proofs of Concept, early implementations, and legacy technology modernization initiatives.
Experience monitoring architectural compliance of data engineering products and pipelines against enterprise standards.
Experience helping data engineering organizations mature their technical capabilities and delivery practices.
Experience establishing or participating in Communities of Practice focused on data engineering, architecture, analytics, or emerging technologies.

Responsibilities

Develop and maintain enterprise data platform architectures that are scalable, reliable, secure, and aligned with business, application, and technology strategies.
Lead the development of enterprise data landscapes, data strategies, target-state architectures, and architectural approaches using established frameworks such as TOGAF, FEAF, or DODAF.
Establish and maintain reference architectures, architecture patterns, standards, and best practices for enterprise data platforms.
Conduct architectural reviews, identify exceptions to established standards, and manage remediation efforts to maintain architectural integrity.
Lead the modernization of enterprise data warehouse and analytics platforms through the adoption of cloud-based data services, analytical technologies, and emerging data capabilities.
Oversee the design and implementation of modern data platform components, including data storage, streaming, orchestration, data processing, and analytics services.
Architect Data, Analytics, and Machine Learning products and pipelines that align with enterprise architecture, data strategy, governance, security, and data-sharing requirements.
Evaluate emerging data technologies through Proofs of Concept, technical demonstrations, codathons, and vendor co-development initiatives.
Lead technology evaluations and support enterprise decision-making by assessing the capabilities, benefits, risks, and feasibility of emerging data platform technologies.
Guide the establishment and implementation of enterprise data platforms across cloud service provider environments, ensuring the technology stack supports both proof-of-concept activities and production data engineering workloads.
Lead RFI/RFP and technology procurement activities related to data platforms, cloud services, and data engineering technologies.
Partner with technology vendors to evaluate, develop, and implement innovative enterprise data platform capabilities.
Design and maintain data services portfolios and data products based on industry standards and organizational data engineering capabilities.
Establish data governance practices covering data quality, metadata management, security, compliance, and responsible use of enterprise data.
Ensure data governance standards are incorporated into data engineering products, Data/Analytics/ML pipelines, and operational processes.
Establish and enforce data security standards and best practices throughout data platforms, pipelines, and analytical products.
Collaborate with engineering, analytics, security, architecture, and business stakeholders to translate strategic objectives into technical solutions and implementation roadmaps.
Provide architectural guidance to data engineering teams and help establish repeatable delivery methodologies, engineering standards, and best practices.
Monitor architectural adherence across data engineering products and pipelines and support teams in improving technical and delivery maturity.
Identify opportunities to modernize legacy data environments and guide technology refresh initiatives.
Research emerging technologies and industry capabilities to identify opportunities for improving enterprise data platform functionality and sustainability.
Support the development of communities of practice and knowledge-sharing initiatives that encourage data-driven decision-making and adoption of modern data engineering practices.
Provide technical leadership, mentoring, and guidance to data architects, engineers, and other technical stakeholders.
Select and evaluate appropriate hardware, software, tools, and lifecycle strategies for data warehouse components, including ETL, metadata management, data profiling, performance monitoring, reporting, and analytics.
Develop and maintain architectural documentation, standards, technical guidance, and best practices for enterprise data environments.