Remote Senior Data Platform Architect - Azure Databricks

Fractal; United States; On-site; USD 150,000 - 170,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Parental Leave

Paid holidays

Job summary

Fractal Analytics is seeking a Solution Architect to design and drive enterprise-scale data engineering architectures on Azure Databricks and cloud platforms. This role defines end-to-end data solutions, architecture patterns, and engineering standards to support analytics, AI/ML, and operational use cases.

The ideal candidate combines deep technical expertise with strong business alignment to enable scalable, secure data platforms and standardized, governed data ecosystems across domains.

Qualifications

• We are seeking a Solution Architect to design enterprise-scale data architectures on Azure Databricks.
• Deep technical expertise with business alignment to enable scalable, secure data platforms.
• Provide architectural oversight for pipelines, workflows, and platform capabilities.

Responsibilities

• Design scalable Lakehouse architectures (Databricks, Delta Lake).
• Develop reference architectures, reusable frameworks, and design patterns.
• Align architecture with performance, scalability, security, and cost goals.
• Define canonical data models, dimensional schemas, and semantic layers.
• Ensure data consistency, interoperability, and reuse across teams.
• Guide engineering teams on Spark optimization and data pipeline best practices.
• Enable multi-cloud integrations when required.

Skills

Lakehouse / Data Warehouse patterns

Azure Databricks

CI/CD / DevOps

Metadata & lineage tools

Multi-cloud integrations

Education

Bachelor’s degree in Computer Science / Engineering

Tools

Unity Catalog

Purview

Delta Lake

Azure DevOps

Git

Job description

Fractal Analytics is seeking a Solution Architect to design and drive enterprise-scale data engineering architectures on Azure Databricks and cloud platforms. This role defines end-to-end data solutions, architecture patterns, and engineering standards to support analytics, AI/ML, and operational use cases.

The ideal candidate combines deep technical expertise with strong business alignment to enable scalable, secure data platforms and standardized, governed data ecosystems across domains.