Azure Data Architect — Data Lakes, Warehouses & Pipelines

Siri InfoSolutions Inc; Detroit (MI); On-site; USD 120,000 - 140,000; Full time

Job summary

Siri InfoSolutions Inc in Plano, TX is seeking a Data Architect to lead enterprise data architecture and modeling on-site. You will define standards, build scalable data models, and guide data governance and security across analytics platforms.

The role centers on the Azure data platform (ADF, Databricks, ADLS, Synapse) and includes collaboration with stakeholders to drive data modernization, performance, and governance across data pipelines and warehouses.

Qualifications

• Experience with enterprise data architecture and modeling.
• Strong background in Azure data services (ADF, Databricks, ADLS, Synapse).
• Experience with data governance, quality, and metadata management.

Responsibilities

• Define enterprise data architecture, standards, and best practices.
• Design scalable data models, data lakes, data warehouses, and data pipelines.
• Lead data strategy, governance, security, and quality initiatives.
• Provide architecture guidance for Azure-based data platforms and analytics solutions.
• Collaborate with business stakeholders, data engineers, analysts, and solution architects.
• Drive performance optimization, scalability, and data modernization programs.

Skills

Python

SQL

PySpark

Data governance

Data quality

Metadata management

Cloud architecture

Security best practices

Azure DevOps

CI/CD

Solution design

Stakeholder management

Terraform

IaC

Tools

Azure Databricks

Azure Synapse

Azure Data Factory

ADLS

Job description

Siri InfoSolutions Inc in Plano, TX is seeking a Data Architect to lead enterprise data architecture and modeling on-site. You will define standards, build scalable data models, and guide data governance and security across analytics platforms.

The role centers on the Azure data platform (ADF, Databricks, ADLS, Synapse) and includes collaboration with stakeholders to drive data modernization, performance, and governance across data pipelines and warehouses.