Data Architect

Plante & Moran•Southfield, MI

3d•$110,000 - $181,000•Hybrid

About The Position

Partner with business stakeholders, technology teams, and leadership to understand objectives, define business and data challenges, and align on practical data and analytics solutions. Lead discovery and solution design efforts, including defining requirements, risks, dependencies, success criteria, target-state architecture, data flows, integration approaches, and implementation roadmaps. Design scalable, secure, and governed enterprise data architectures using Microsoft Fabric, Azure Data Services, Databricks, Power BI, and related modern data platform technologies. Establish and reinforce architecture standards, Medallion Architecture patterns, development practices, data modeling approaches, CI/CD processes, testing, observability, and operational support expectations. Provide technical leadership across multiple concurrent initiatives, translating architecture designs into executable delivery plans and guiding teams through design, build, testing, deployment, and handoff. Evaluate technical tradeoffs related to scalability, performance, maintainability, cost, security, governance, and long-term supportability while ensuring alignment with enterprise standards. Serve as a trusted advisor and escalation point for complex solution decisions, helping stakeholders navigate ambiguity, balance competing priorities, and reach consensus on the path forward. Mentor and supervise a small team of data engineers while modeling accountability, collaboration, continuous improvement, and responsible use of AI-assisted productivity tools.

Requirements

Bachelor's degree in Software Engineering, Computer Science, Data Science, or related area required.
5-8 years of experience in data architecture, data engineering, analytics engineering, business intelligence, or related technical roles.
Proven experience designing and delivering enterprise-scale data and analytics solutions from discovery through implementation and production support.
Strong hands-on experience with Microsoft Fabric, including Lakehouse, Data Engineering, Data Factory, OneLake , and semantic model concepts.
Experience designing modern lakehouse , warehouse, and data integration architectures using Microsoft Fabric, Azure Data Services, Databricks, and Power BI.
Strong working knowledge of Azure Data Factory, Azure Data Lake Storage, Azure SQL, SQL/T-SQL, PySpark , and Spark-based processing.
Deep understanding of data warehousing, dimensional modeling, Medallion Architecture, data lifecycle management, and pipeline reliability.
Experience establishing engineering standards for data pipelines, source control, CI/CD, testing, pull request reviews, observability, and operational support.
Familiarity with GitHub, GitHub Actions, API integrations, data governance and lineage concepts, and legacy data platforms such as Informatica and SSIS.

Responsibilities

Partner with business stakeholders, technology teams, and leadership to understand objectives, define business and data challenges, and align on practical data and analytics solutions.
Lead discovery and solution design efforts, including defining requirements, risks, dependencies, success criteria, target-state architecture, data flows, integration approaches, and implementation roadmaps.
Design scalable, secure, and governed enterprise data architectures using Microsoft Fabric, Azure Data Services, Databricks, Power BI, and related modern data platform technologies.
Establish and reinforce architecture standards, Medallion Architecture patterns, development practices, data modeling approaches, CI/CD processes, testing, observability, and operational support expectations.
Provide technical leadership across multiple concurrent initiatives, translating architecture designs into executable delivery plans and guiding teams through design, build, testing, deployment, and handoff.
Evaluate technical tradeoffs related to scalability, performance, maintainability, cost, security, governance, and long-term supportability while ensuring alignment with enterprise standards.
Serve as a trusted advisor and escalation point for complex solution decisions, helping stakeholders navigate ambiguity, balance competing priorities, and reach consensus on the path forward.
Mentor and supervise a small team of data engineers while modeling accountability, collaboration, continuous improvement, and responsible use of AI-assisted productivity tools.